Huazhong University of Science and Technology (HUST) is welcoming students from Oxford University to take part in research internships in Wuhan, China. This year, HUST offers 31 projects, including 61 positions in total, which provide interns with challenging and meaningful internship experiences and Chinese cultural immersion classes over the summer.

The duration of 2025 Summer internship program is from the June 30th to August 8th, 2025 (For registration: June 28th to June 29th, 2025). 

The 31 projects are:

Internship in Chinese Governance and Society

① College of Public Administration

#1 Dr. Zheng Linzi’s research team (Positions: 1-3)

#2 Prof. Cheng Chen’s research team (Positions: 1-3)

#3 Prof. Seah Shuo’s research team (Position: 1-3)

#4 Prof. Zhang Yi’s research team (Position: 1-3)

Internship in Scientific Research

① China-EU Institute for Clean and Renewable Energy

#1 Prof. Guo Limin’s research team (Position: 3)

#2 Prof. Luo Xiaobing’s research team (Positions: 2)

#3 Prof. Hu Song’s research team (Positions: 3)

#4 Prof. Chen Rong’s research team (Position: 1)

#5 Prof. Chen Rong’s research team (Position: 1)

#6 Prof. Chen Rong’s research team (Position: 1)

#7 Associate researcher Li Huayao’s research team (Positions: 2)

#8 Prof. Chen Huanxin’s research team (Positions: 3)

#9 Prof. Liu Xiaowei’s research team (Positions: 2)

#10 Prof. Zhao Yongchun’s research team (Positions: 3)

#11 Prof. Luo Cong’s research team (Positions: 3)

#12 Prof. Yang Qing’s research team (Position: 1)

#13 Prof. Yang Jun’s research team (Positions: 2)

② School of Electronic Information and Communications

#1 Prof. Yang Peng’s research team (Position: 1)

#2 Prof. Liu Qiong and Prof. Yang You’s research team (Position: 1)

#3 Prof. Kai Wan’s research team (Positions: 1-2)

#4 Prof. Caiming Qiu’s research team (Positions: 1-2)

#5 Prof. Zhenyu Liao’s research team (Positions: 1-2)

#6 Prof. Zenan Ling’s research team (Positions: 1-2)

#7 Dr. Ke Xian’s research team (Positions: 1-2)

#8 Prof. Ge Xiaohu’s research team (Positions: 1-2)

③ Wuhan National High Magnetic Field Center

#1 Prof. Han Xiaotao’s research team (Positions: 1-2)

#2 Prof. Han Xiaotao’s research team (Positions: 1-2)

#3 Prof. Gang Xu’s research team (Position: 1)

④Wuhan National Laboratory for Optoelectronics

#1 Prof. TANG Jiang’s research team (Positions: 1)

#2 Prof. LU Peixiang’s research team (Positions: 1)

#3 Prof. Wang Jian’s research team (Position: 1)

Year of 2024 marks the 10th anniversary of "HUST-Oxford" Summer Internship Program and the University of Oxford specially built up the webpage to celebrate the partnership between two universities: https://www.careers.ox.ac.uk/article/10-years-of-internships-at-hust 

HUST also specifically produced a commemorative video for celebrating the 10th anniversary of "HUST-OXFORD" Summer Internship Program: 

http://discover.hust.edu.cn/fore/courses/courseDetail.do?sid=24120614403026313531626 

Besides, please feel free to watch our promotional video for 2024 "HUST-OXFORD" Summer Internship Program: 

http://discover.hust.edu.cn/fore/courses/courseDetail.do?sid=24110711385333159565845

Internship in Chinese Governance and Society

College of Public Administration

#1 Dr. Zheng Linzi’s research team (Positions: 1-3)

Introduction to the project:

The rapid adoption of digital technologies has transformed urban economies, enhancing their resilience to external shocks such as the COVID-19 pandemic. Cities with robust digital infrastructures and literacy managed remote work, online education, and e-commerce effectively, ensuring quicker economic recovery. Technologies like AI, IoT, and cloud computing enable agility and efficiency, while digital platforms expand SME market access. However, challenges like the digital divide and cybersecurity risks persist, requiring insights into how cities leverage digital tools and implement policies to enhance economic resilience.

Project Aims:

This study aims to investigate how digital technologies influence urban economic resilience by analyzing their role in mitigating the impacts of external shocks and exploring strategies to optimize their deployment for sustainable urban growth.

Methods:

1. Econometric Modeling

2. Big Data Analytics

3. Geospatial Analysis

4. Survey and Index Construction

Learning outcomes:

1. Gain a comprehensive understanding of how digital technologies contribute to urban economic resilience in the face of disruptions.

2. Knowledge about how to identify critical factors that influence the successful integration of digital technologies in urban economies, including infrastructure, digital literacy, and policy frameworks.

3. Research abilities to evaluate the effectiveness of various digital strategies and policies in enhancing resilience, highlighting best practices and potential areas for improvement.

Project summary:

The project will deliver a framework linking digital transformation to urban resilience, alongside actionable recommendations for policymakers and urban planners. These findings aim to support cities in leveraging digital technologies to build more adaptive, inclusive, and sustainable economies.

Assessment:

Working paper and oral presentation is required by the end of internship.

Applicant profile:

1. We are looking for undergraduate interns with an interest in urban studies, digital technologies, and economic resilience.

2. Interns should have a strong desire to learn, research potential, and good academic performance.

3. Basic knowledge of urban infrastructure, policy analysis, or digital tools is preferred but not mandatory.

4. Proficiency in English is required. Knowledge of other languages is welcome but not essential.

#2 Prof. Cheng Chen’s research team (Positions: 1-3)

Introduction to the project (lab):

Leadership has always been an important issue in the public sector. In recent years, digital technologies have had a profound impact on the structural characteristics and communication processes of human organizations, as well as on leadership processes. Public sector leadership is also undergoing digital transformation, and its main characteristics and influencing factors still need to be further studied and explored.

This project will conduct theoretical analysis from multidisciplinary perspectives such as psychology, organizational behavior and public administration, and conduct empirical analysis through case analysis, social survey, situational experiment and other research methods to explore issues related to digital leadership in the public sector.

Project Aims:

This project aims to analyze the core attributes and implications of digital leadership in the public sector, along with the organizational and individual elements that affect the digital leadership process and the digital competence of officials in government and other departments, so as to offer valuable insights for human resource management and organizational construction in the public sector.

Methods:

1.Questionnaire survey

2.Situational experiment

3.Quantitative analysis

Learning outcomes:

By the end of this project interns should have gained:

1. Understand the core attributes, influencing factors and implications of digital leadership in the public sector

2. To enhance the application of multidisciplinary knowledge in psychology, organizational behavior and public administration

3. Strengthen the application of research methods such as case analysis, questionnaire survey and situational experiment.

Project summary:

This project aims to analyze the key characteristics, influencing factors and implications of digital leadership in the public sector.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The intern has a strong interest in leadership, particularly digital leadership in the public sector.

3. They have knowledge of subjects such as public administration, organizational behavior, or psychology.

4. They have dabbled in questionnaire survey, situational experiment and other research methods.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#3 Prof. Seah Shuo’s research team (Position: 1-3)

Introduction to the project (lab):

1. Background of the project.

With the acceleration of global urbanization, emergency management and public safety issues have become a focus of social attention. From terrorist attacks, natural disasters to criminal incidents, they can all pose serious threats to public safety. How to detect and effectively respond to risks related early on has become an important research topic for emergency managers and various sectors of society. The development of big data technology has provided new ideas and methods for public safety risk warning research.

2. The content of project

This project focuses on the study and application of the latest technologies in emergency management and public safety early warning, analyzing governance models supported by big data in emergency warning and management.

Project Aims:

By this project, we plan to integrate the concepts of refinement, intelligence, and scientificity into the entire process of emergency management before, during, and after the crisis, promoting the high integration of public emergency management and big data technology; Applying the latest achievements of big data to the governance of natural disasters, accident disasters, public health incidents, and social security incidents enables data support for prevention preparation, prediction and early warning, decision-making response, and rescue recovery in public crisis governance.

Methods:

1. Risk analysis

2. Data mining

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of the relationship between public safety and big data

2. Learning the methods of risk analysis.

3. An ability in material characterization and data analysis.

Project summary:

One of the advantages of big data technology is correlation analysis, which evaluates and predicts security risk points by comparing and analyzing the security risk correlation of various types of data.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of public safety and big data analysis and have already demonstrated strong research potential.

3. In addition, they should have studied the basic knowledge about a range of fields in public administration and data mining software.

4. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#4 Prof. Zhang Yi’s research team (Position: 1-3)

Introduction to the project (lab):

1. Background of the project.

This project will focus on the digital transformation of governments. After entering the digital age, it is a challenging issue how to effectively use advanced digital technology to transform governance paradigm, so as to improve government governance efficiency and enhance public trust in the government, which is of important theoretical and practical value.

2. The content of project is to learn the theory of government digital transformation, investigate the real cases of government digital transformation, and master the methods to carry out in-depth research on government digital transformation.

Project Aims:

The research objective of this project is to clarify the driving factors and mechanism of government digital transformation, analyze the impact of government digital transformation on governance effectiveness, and propose the optimal path of government digital transformation.

Methods:

1. Case study

2. Quantitative analysis method

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of the relationship between digital technologies and governance paradigms.

2. Learning the underlying mechanisms of digital transformation of governments.

3. An ability to analyze multiple cases to extract generalized theories.

Project summary

This project will clarify the mechanism, impact and path of digital transformation of governments

Assessment

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of public administration as well as digital technology, and have already demonstrated strong research potential.

3. In addition, they should have studied the basic knowledge about a range of fields in social science, particularly public administration and public policy.

4. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

Internship in Scientific Research

①China-EU Institute for Clean and Renewable Energy

#1 Prof. Guo Limin’s research team (Position: 3)

Introduction to the project (lab):

This project provides a scheme of CO₂ catalytic conversion. CO₂ has attracted a lot of attention over the last 20 years due to the link between the increases in CO₂ emissions with the rise in global temperatures and especially recently high motivation for carbon neutral society scenarios (2050 for EU and Japan; 2060 for China). Rather than viewing the CO₂ as a waste material, the conversion of CO₂ to value added products, such as methane, methanol, ethanol, olefin and other hydrocarbons, has attracted much attention.

Project Aims:

The aims of this project are to catalyze CO₂ to methanol. By the means of catalytic pathway design and catalysts synthesize to achieve the high activity and selectivity. Moreover, our lab also aims at better understanding CO₂ activation and catalytic mechanism by using variety of characterization methods.

Methods:

This project applies the methods including:

-catalyst preparation

-catalyst characterization: X-Ray Diffraction, Scanning electron microscope, Transmission electron microscope, in-situ diffuse reflectance fourier transform infrared spectroscopy etc.

-catalytic activity measurements

Learning outcomes:

By the end of this project interns, the student can learn:

1. An ability in catalyst preparation.

2. An ability in catalyst characterization.

3. An ability in catalytic activity measurements.

Project summary：

This project is significant for the utilization of CO₂ as the feedstock for high valued molecules synthesis through the heterogeneous catalytic process.

Assessment：

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns. The interns should have an interest in the field of chemistry (undergraduate) and have already demonstrated strong research potential (graduate).

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly chemistry, nanomaterials and so on (undergraduate).

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#2 Prof. Luo Xiaobing’s research team (Positions: 2)

Introduction to the project(lab):

This project provides new ideas for the thermal solutions under two scenarios: the miniaturization of electronic devices and the high-temperature environment. In the confined space, the performance requirements of micro pump, a key driving component of the liquid cooling system, are fulfilled. In high temperature environment, the protection of electronic devices is achieved.

Project Aims:

This internship project aims to (1) design and optimize a micro pump; (2) design heat dissipation for electronic devices in high temperature environment.

Methods:

This project applies the methods of (1) fluid simulation; (2) heat transfer simulation.

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of hydrodynamic and heat transfer simulations and processes.

2. A skill in usage of simulation software.

3. An ability in paper writing and oral presentation.

Project summary

This project is significant for the thermal management of electronic devices.

Assessment：

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of energy utilization, thermal management technology and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly heat transfer, hydrodynamics, mathematics and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#3 Prof. Hu Song’s research team (Positions: 3)

Introduction to the project (lab):

This project aims to explore innovative approaches to the synergistic utilization of multi-energy systems for biomass gasification and hydrogen production. With the continuous development of agricultural and forestry industries, the accumulation of biomass residues has increased, posing significant environmental challenges if not treated properly. However, biomass residues, such as agricultural waste, forestry by-products, and organic materials, represent valuable renewable resources. Efficiently converting these residues into high-value products like hydrogen can simultaneously advance industrial sustainability and contribute to ‘carbon neutrality’.

The research team includes experienced professionals and has a strong track record of hosting international students, including 10 from Oxford University and 13 others from various institutions. Students with interests in energy and biomass utilization are encouraged to participate in this project.

State-of-the-art experimental facilities and a highly supportive research environment are available, ensuring an optimal learning and research experience.

Project Aims:

This internship project aims to investigate the potential of biomass gasification for hydrogen production in China. Interns will be involved in process simulation, conducting economic and environmental assessments, and understanding the market potential of the technology. You will also get hands-on experience with scientific research instruments and observe how biomass is converted into gas, including hydrogen.

Interns will learn about the basics of biomass conversion, novel thermal conversion technologies, and the practical steps involved in bringing this technology to commercial application. At the same time, we will also organize the investigation of the resources and environment around Wuhan city.

Methods:

This project applies the following methods:

1. Laboratory visits and experimental observations,

2. Systematic data acquisition and research,

3. Process modeling and computational simulations,

4. Comprehensive data analysis to evaluate performance metrics.

Learning outcomes:

By the end of this project interns should have gained:

1. Gain a comprehensive understanding of biomass gasification and hydrogen production processes.

2. Acquire skills in data collection, analysis, and interpretation.

3. Develop proficiency in using process simulation and life cycle assessment software.

4. Enhance their capabilities in academic writing and technical presentations.

Project summary:

The utilization of multi-energy systems for biomass gasification and hydrogen production represents a critical advancement in energy conversion technologies and the high-value utilization of biomass resources. This project contributes to the ongoing development of sustainable energy solutions, offering participants a unique opportunity to engage with cutting-edge research while acquiring valuable technical and analytical skills.

Assessment：

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of energy utilization, chemical engineering, thermal conversion technology and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy, chemistry, physics, mathematics and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#4 Prof. Chen Rong’s research team (Position: 1)

Introduction to the project(lab):

For hydrogen fuel cell, the activity and durability of carbon supported Pt based electrocatalysts are unchanging goals for the widespread application. Atomic layer deposition is known for its atom-level control accuracy over the film growth on substrates or NPs based on self-limiting chemical half-reactions. This project provides new ideas to improve the activity and durability of Pt catalyst for hydrogen fuel cell based on atomically surface and interface modification on Pt nanoparticle.

Project Aims:

This internship project aims to minimize the reliance on precious metals in hydrogen fuel cells, thus optimizing their performance.

Methods:

Research on the preparation technology of high-efficiency precious metal catalysts for hydrogen fuel cells based on micro/nano particle atomic layer deposition technology, to achieve the reduction of precious metals in hydrogen fuel cells.

Learning outcomes:

By the end of this project interns should have gained: 1) Deep understanding of the application of micro/nano particle atomic layer deposition technology in hydrogen fuel cells. 2) A skill in usage of preparation and characterization equipment of membrane electrode assembly for fuel cell. 3) An ability in paper writing and oral presentation.

Project summary:

Based on atomic layer deposition technology, develop a powerful technique to coat high-energy particles and noble metal nanoparticles to provide efficient precious metal catalysts for hydrogen fuel cells.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of energy and electronics, heat and mass tranfer processes, micro/nano fabrication, Atomic Layer Deposition and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy, thermodynamics, chemistry, physics, engineering and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#5 Prof. Chen Rong’s research team (Position: 1)

Introduction to the project(lab):

Perovskite solar cells (PSCs) have attracted significant attention due to their fast and low-cost solution fabrication, where electron transport layer (ETL) and hole transport layer (HTL) shall be optimized to achieve high efficiency. Atomic layer deposition is known for high quality and uniform nanoscale thin film due to the self-limiting chemical half-reactions. This project provides new ideas to improve the electrical and optical property of ETL and HTL for perovskite solar cells through ALD.

Project Aims:

This internship project aims to deposit high quality ETL or HTL for perovskite solar cells devices, thus optimizing their performance.

Methods:

Research on the preparation technology of functional thin films in perovskite solar cells solar cells based on thermal and spatial atomic layer deposition.

Learning outcomes:

By the end of this project interns should have gained: 1) Deep understanding of the application of ETL/HTL atomic layer deposition technology in perovskite solar cells. 2) A skill in usage of preparation and characterization equipment of ETL/HTL films for perovskite solar cells. 3) An ability in paper writing and oral presentation.

Project summary

Based on atomic layer deposition technology, develop a powerful technique to deposit high quality ETL/HTL films for perovskite solar cells.

Assessment

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of energy and electronics, heat and mass tranfer processes, micro/nano fabrication, Atomic Layer Deposition and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy, thermodynamics, chemistry, physics, engineering and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#6 Prof. Chen Rong’s research team (Position: 1)

Introduction to the project(lab):

Hydrogen fuel cell based on proton exchange membrane technology is a high-performance, environmentally friendly alternative to fossil fuel-based cell. The heat and mass transfer properties of electrolyte species are critical to the electrochemical performance and thermal management of the fuel cell. Understanding the fundamental heat and mass transfer mechanism in the nanoconfined space within the proton exchange membrane is of critical importance to the design and development of the hydrogen fuel cells.

Project Aims:

This internship project aims to establish atomistic model for the heat and mass transfer processes of electrolyte within the proton exchange membranes of the hydrogen fuel cells.

Methods:

Research on the mass and heat transfer in the membrane of fuel cell using molecular dynamics simulation, providing numerical insight on the performance and thermal management of the fuel cell.

Learning outcomes:

By the end of this project interns should have gained: 1) Fundamental understanding of nanoscale heat and mass transfer process. 2) Proficiency in atomistic modeling and non-equilibrium MD calculations on transfer properties in fuel cells. 3) An ability in paper writing and oral presentation.

Project summary:

Based on atomistic modeling and molecular dynamics simulations, investigate the fundamental mechanisms of heat and mass transfer of the electrolyte in the proton exchange membrane of the hydrogen fuel cells.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of energy and electronics, heat and mass tranfer processes, micro/nano fabrication, Atomic Layer Deposition and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy, thermodynamics, chemistry, physics, engineering and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#7 Associate researcher Li Huayao’s research team (Positions: 2)

Introduction to the project(lab):

Background of the project.

Hydrogen power has emerged as a promising solution to combat global warming due to its high energy density (120–142 MJ/kg), renewable nature, and zero carbon emissions during combustion. In particular, hydrogen energy vehicles have attracted public attention because of their advantages, including zero pollution, zero emissions, high storage capacity, and sufficient power. However, the technical challenges associated with the safe storage and utilization of hydrogen remain significant due to the low minimum ignition energy (0.019 mJ), high combustion heat (142 kJ/g), and wide flammability range (4%–75%)  Real-time monitoring of hydrogen leakage is crucial throughout the entire processes of production, storage, and transportation.

Artificial olfaction, known as machine olfaction or electronic nose technology, refers to the creation of systems that mimic the human sense of smell using electronic sensors and pattern recognition algorithms. Much like how our sense of smell works through detecting and recognizing different odors, artificial olfaction aims to replicate this capability using technology.

The application of electronic nose technology in the hydrogen energy field is primarily manifested in the monitoring of industrial safety. The electronic nose can perform real-time monitoring of hydrogen and its mixed gases by integrating various gas sensors. Its high sensitivity and rapid response capability enable the electronic nose to effectively identify weak signals of hydrogen leaks and issue corresponding alarms, thereby ensuring the safe operation of hydrogen energy systems. These characteristics make the electronic nose an important detection and monitoring tool in the hydrogen energy industry, contributing to the safe and efficient application of hydrogen energy.

The electronic nose is a pivotal detection and monitoring asset in the hydrogen sector, ensuring the safe and efficient utilization of hydrogen energy. It strives to boost sensitivity, precision, and adaptability, enabling it to tackle more intricate olfactory challenges and profoundly impacting a range of industries through its machine-aided olfactory detection and identification capabilities

The content of the project

(1) Design and Synthesis of Hydrogen-Sensitive Materials

(2) Design and Simulation of Field-Effect Transistor Sensors

(3) Deep learning algorithms design

(4) Electronic nose assembly and application

Project Aims:

The aim of this project is to to realize an intelligent artificial olfactory system, and the project aims to achieve real-time monitoring throughout the entire process of hydrogen production, storage, transportation, and usage, as well as to promote the development of the hydrogen energy application field.

Methods:

1)Material synthesis and characterization

2)Device simulation and fabrication (by TCAD)

3)Algorithm design (by Python)

4) Data analysis (by Python and SPSS)

5)Algorithm design (by Python)

6)Data analysis (by Python and SPSS)

Learning outcomes:

By the end of this project interns should have gained:

1) An ability in logical thinking and systematic design

2) An ability in material synthesis, device fabrication and data analysis

3) An ability in paper writing and oral presentation.

Project summary:

This project focuses on the realization of artificial olfactory, including material synthesis (chemical synthesis), Micro-nano fabrication technology (Field-Effect Transistor), algorithm design (Python) and data analysis(SPSS or Python), and application in environment monitoring and breath analysis.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the field of materials, semiconductor device, or algorithm and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly ( chemistry, semiconductor physics or computer science ) .

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#8 Prof. Chen Huanxin’s research team (Positions: 3)

Introduction to the project(lab):

Background of the project.

This project will focus on the hot issue of high energy consumption of building HVAC systems. In the building system, the energy consumption of Heating, Ventilation and Air Conditioning (HVAC) system accounts for 40%-60% of the energy consumption of the whole building system. Therefore, it is of great significance to construct an energy consumption prediction model for the internal air conditioning system of the building and realize high precision energy consumption prediction.

In recent years, with the development of technologies such as the Internet of Things, the measured data collection level of HVAC system is constantly improving, but there are still problems such as data quality that limit the development of energy consumption prediction. At the same time, in 2021, the Ministry of Housing and Urban-Rural Development issued a document pointing out that it is necessary to promote intelligent production, promote intelligent construction, develop digital design, build Internet platforms for the construction industry and accelerate the digital transformation and upgrading of the construction industry. Therefore, it is worth studying how to improve energy consumption prediction effect, promote energy conservation and carbon reduction of buildings and improve energy efficiency of buildings based on new digital technologies such as big data and artificial intelligence under existing deficiencies.

Therefore, this project will take building HVAC system as the research object, develop energy consumption prediction model, effectively analyze its energy performance and energy saving potential, and control building operation mode, improve energy utilization efficiency and reduce energy consumption and carbon emissions.

The content of project.

To develop building HVAC energy consumption prediction system, it is necessary to integrate geometeorological information and building operation information, collect the operation data of HVAC system, display the operating status of equipment in real time, and carry out automatic analysis and information feedback. The project needs to achieve two important functions:

1) Detect abnormal running state of HVAC system and provide warning.

2) Real-time collection and prediction of energy consumption data of building HVAC system.

An energy consumption forecasting system need to be established from four aspects: data acquisition, data calculation, data display and data application. Data acquisition is when sensors of HVAC systems and equipment send real-time data to computing servers via the Internet of Things according to specified communication protocols. Data calculation is to use big data analysis method and machine learning algorithm to analyze real-time data and give results, such as abnormal alarm information, energy consumption distribution information, energy consumption prediction results, optimization control signals, etc. For example, real-time monitoring of HVAC system, abnormal alarm and record, equipment asset information, energy consumption prediction results, energy saving and optimization control history, comprehensive statistical analysis report, etc. Data display is the display of all information from the system in the form of charts, tables and graphs, etc. Data application refers to the evaluation of future energy saving measures or other improvement plans by managers or decision makers based on available information.

Project Aims:

This internship project aims to develop energy consumption prediction model of building HVAC system, realize data acquisition and transmission, and carry out real-time monitoring and control of system operation and energy consumption. At the same time, when system abnormalities and excessive energy consumption are found and demand side responds to demand, timely warning and control can be achieved, and real-time energy consumption prediction can be made to predict short-term energy consumption with high precision.

Methods:

1. The operation and maintenance system software of this project is mainly written as Python.

2. The visual design of vehicle and subway station structures in this project can be done by using EnergyPlus and other software.

3. The object of this project is building HVAC system, and the main method is big data analysis. Therefore, big data analysis and machine learning algorithm are used widely in this project.

4. The air conditioning equipment data and communication protocol will be provided. Energy consumption prediction methods will be trained and guided by our research group.

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of building HVAC system.

2. Learning the real cooling system of commercial building.

3. Deep understanding of how to apply big data analysis and artificial intelligence method into energy consumption prediction system.

4. An ability in programming based on python.

5. An ability in designing energy prediction model of building HVAC system based on EnergyPlus and other software.

Project summary：

The intelligent operation and maintenance system of building air conditioning system is meaningful and significant for energy saving. The project will be interesting for interns, and we are looking forward to your joining.  

Assessment：

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of big data analysis, machine learning, smart city, intelligent operation and maintenance of HVAC systems and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly mechanical equipment, heating, ventilation and air condition system, programming, big data analysis.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#9 Prof. Liu Xiaowei’s research team (Positions: 2)

Introduction to the project(lab):

This project provides new ideas on the utilization of stable combustion in coal blended with hydrogen/ammonia, along with the understanding and regulation methods concerning the formation of pollutants. Utilizing a 50kW self-stabilizing one-dimensional furnace and a sophisticated high-temperature laminar settling furnace reactor equipped with controllable atmosphere conditions, this investigative endeavor meticulously elucidates the intricate combustion field characteristics across the entire spectrum of ignition, combustion, and burnout for coal blended with hydrogen/ammonia. Furthermore, it delves into the detailed formation mechanisms and patterns of pollutants, encompassing NO_x, PM2.5, and SO_x.

Project Aims:

This internship project aims to study the combustion field characteristics and pollutant formation patterns throughout the entire process of coal blended with hydrogen and ammonia.

Methods:

Hydrogen energy has played a pivotal role in advancing human society, earning seven Nobel Prizes in related fields. However, integrating wind and solar power into the grid poses significant challenges and requires substantial investments. To effectively harness this renewable energy, green hydrogen and green ammonia produced through green electricity emerge as promising low-carbon energy solutions. This project leverages a 50kW self-stabilizing combustion experimental platform to conduct in-depth research on the combustion characteristics of coal blended with hydrogen/ammonia, encompassing flame behavior, temperature distribution along the combustion path, and emissions of pollutants such as PM2.5, NO_x, and SO_x.

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of experiments conduction and processes.

2. A skill in usage of using a 50kW self-stabilizing combustion experimental platform and high-temperature laminar settling furnace reactor.

3. An ability in paper writing and oral presentation.

4. Capability for comprehensive in-situ gas sampling and analysis, coupled with expertise in collecting and analyzing particulate matter at the tail end.

Project summary：

This project is significant for the utilization of hydrogen energy / ammonia energy

Assessment：

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of hydrogen energy and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy, chemistry, physics.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#10 Prof. Zhao Yongchun’s research team (Positions: 3)

Introduction to the project(lab):

Background of the project.

This project will focus on photocatalytic CO₂ reduction. CO₂ actively responds to the energy crisis and greenhouse effect. Driven by the sunlight, the technology of  photocatalytic CO₂ reduction could converse CO₂ into renewable hydrocarbon fuel under mild reaction conditions.

The content of project is learning the methods of catalyst synthesis, characterization, experimental design and data analysis.

Project Aims:

This internship project aims to develop new and efficient photocatalyst for photocatalytic CO₂ reduction, design new photocatalytic conversion pathways for CO₂, and reveal the mechanism for photocatalytic CO₂ reduction.

Methods:

1. Catalyst synthesis

2. In-situ characterization

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of the technology of photocatalytic CO₂ reduction.

2. Learning the methods of materials synthesis.

3. An ability in material characterization and data analysis.

Project summary:

This project is advanced materials synthesis for photocatalytic CO₂ reduction and mechanism unveiling.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the field of photocatalysis and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly chemistry, nanomaterials.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#11 Prof. Luo Cong’s research team (Positions: 3)

Introduction to the project(lab):

This project will focus on CO₂ capture and utilization technologies.The content of project is to learn knowledge about capture and utilization of carbon dioxide, including adsorbent technology, chemical looping, oxygen-enriched combustion, CO₂ conversion and utilization, etc.

Project Aims:

The aim of this project is to realize the issue of Global Warming and Greenhouse Effect and the importance of controlling carbon dioxide emissions.

Methods:

Experimental and simulation works in developing carbon capture and utilization technologies, including the design of CO₂ sorbent, solvent, and oxygen carriers, operation of lab-scale bench tests.

Learning outcomes:

By the end of this project interns should have gained:

1.Understanding of carbon capture and utilization technologies

2.Learning the CO₂ capture demonstration projects in China

Project summary:

This project is to report on the research progress in CO₂ capture and utilization in China.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns. Students from Singapore, Malaysia, Thailand, China, as well as those who love Chinese cultures, are highly expected, and we will organize additional tours in or around Wuhan city in one weekend.

The interns should have an interest in the field of CO₂ capture and utilization, and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly energy engineering, chemical engineering, mechanical engineering, economics and so on.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#12 Prof. Yang Qing’s research team (Position: 1)

Introduction to the project(lab):

The Energy and Economics lab specializes in the study of energy systems, climate change, resource management, and environmental interactions. The lab has built a multidisciplinary team of students proficient in energy system modeling, leveraging big data and artificial intelligence techniques. Their work also covers areas such as Life Cycle Assessment (LCA), renewable energy potential evaluation, and the analysis of environmental and economic impacts. Over time, the team has developed significant expertise and made pioneering contributions in carbon footprint calculation and energy system transformation pathways. The team has successfully completed numerous national key scientific research projects and published over 100 papers in top-tier journals, including PNAS and Nature Communications.

The project we are currently working on is the environmental impact assessment of renewable energy, especially photovoltaic solar energy generation units. We focus on the impacts of renewable energy development on the environment in China, and its feedback on carbon emissions.

Project Aims:

This internship project aims to assess the environmental impacts of renewable energy development in China, with a primary focus on solar energy. The project seeks to explore the relationship between the rapid growth of renewable energy and its environmental consequences. The findings will provide valuable insights and recommendations for policymakers to help design a more sustainable development pathway for renewable energy in China.

Methods:

1. Geographic Information System (GIS)

2. Remote sensing (RS)

3. Python programming

4. Basic machine learning algorithms

5. Cloud computing platform, mainly Google Earth Engine

Learning outcomes:

By the end of this project interns should have gained:

1. An ability in using GIS software

2. A basic understanding of cloud computing platforms such as Google Earth Engine

3. Skills in reading and writing Python programs

4. Experience and skills in scientific paper reading and writing

Project summary:

The development of renewable energy is crucial for achieving net-zero emissions and mitigating the effects of climate change. However, the construction of renewable energy infrastructure, such as solar farms, can also result in negative environmental impacts, including effects on biodiversity, food security, and water availability. Therefore, it is essential to quantify these environmental impacts.

This project will focus on evaluating the carbon footprints of photovoltaic solar energy generation units in China. It aims to identify carbon-emission characteristics of large-scale solar energy installations using remote sensing imagery. By quantifying the environmental impacts of these commercial solar units, the project will provide policymakers with a comprehensive view of the rapid expansion of solar farms and their environmental consequences.

We welcome interns with a basic understanding of renewable energy and an interest in GIS, machine learning (image recognition), cloud computing, and Python programming. We look forward to having you join the project!

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the field of renewables and environment interactions and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly renewable energy and machine learning.

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#13 Prof. Yang Jun’s research team (Positions: 2)

Introduction to the project(lab):

This project provides idea on the establishment of a coupled production and deployment system of nuclear energy and hydrogen energy. Nuclear power plant, especially ones with small modular reactor, can be linked to a hydrogen generation plant with hydrogen storage equipment. Both energy can be coupled to deal with the electric grid need.

The contents of project:

To investigate nuclear-hydrogen coupled energy system and demonstrate the operation mechanism of a conceptual design of a nuclear power plant coupled with hydrogen production and storage system.

Project Aims:

This project provides an opportunity to build a simple model to demonstrate the operation mechanism of an innovation design of nuclear-hydrogen coupled energy system.

Methods:

This project requires to establish a model, with MATLAB, Modelica or other tools, to reflect the operation of innovation design of nuclear-hydrogen coupled system.

Learning outcomes:

By the end of this project interns should have gained:

1. Understanding the idea of nuclear-hydrogen coupled energy systems.

2. An ability in using some multi-dimensional software to do basic 2D or 3D design.

3. An ability in programming to perform fundamental energy conversion calculation.

Project summary:

This project is a training to study and establish a simple demonstration model to show the operation mechanism of a nuclear-hydrogen coupled energy system.

Assessment:

Oral presentation is required by the end of internship.

Online feedback is required when the oral presentation is finished.

Applicant profile:

We are interested in the undergraduate and graduate interns.

The interns should have an interest in the fields of hydrogen energy production and utilization and have already demonstrated strong research potential.

In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly thermodynamics, heat transfer and so on (undergraduate).

It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

②School of Electronic Information and Communications

#1 Prof. Yang Peng’s research team (Position: 1)

Introduction to the project (lab):

1. Background of the project.

The prototype of the Metaverse originated from the fiction novel Snow Crash, demonstrated a vivid universe created by computers. Metaverse has revolutionized the way in which humans interact with multimedia through intensive visual and auditory stimulation, making users deeply immersed in a virtual world. The fundamental technologies behind Metaverse are Virtual Reality (VR), multimedia networking, and human-machine interaction.

2. The content of project is key technologies and interesting ideas on Multimedia Communications and Networking for Metaverse.

Project Aims:

This internship project aims to:

1. Learning the fundamentals of multimedia networking, including network architecture and transmission protocols

2. Getting familiar with Virtual reality (VR) video coding, including multi-view cameras, mapping and coding

3. Understanding mobile edge computing, including end-edge-cloud coordinated framework, communication resource allocation.

Methods:

1. Literature review

2. Interactive VR system development and Prototyping

3. Theoretical analysis and transmission scheme design

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of the technology of virtual reality

2. Learning the transmission protocols of multimedia networking

3. Prototyping skills of real VR systems

Project summary：

This project is on the enabling technologies of Multimedia communications and networking for Metaverse.

Assessment：

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of multimedia communications and have already demonstrated strong research potential.

3. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly Calculus and Probability Theory.

4. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#2 Prof. Liu Qiong and Prof. Yang You’s research team (Position: 1)

Introduction to the project (lab):

1. Background of the project.

Recently, Neural radiance fields (NeRFs) have drawn great attention and are now important cornerstones of metaverse and augmented reality research, as is their stronger efficiency and more imaginative rendering performance.NeRFs refer to a suit of deep neural networks that are used to learn and represent objects or scenes. Generally speaking,NeRFs have five main characters: volumetric rendering, novel view synthesis, factorizable embedded space, multi-view consistency and weighted importance sampling.In this project, we mainly use nerf for 3D reconstruction work.

2. The content of project is learning the current state of research on neural radiation fields, how they work and how to train a neural network model that meets the metrics by using an existing training set.

Project Aims:

This internship project aims to train a neural radiation field that can clearly represent a 3D model and optimize the neural network to improve its performance in a certain way.

Methods:

1. Training 3D models with neural networks

2. Representing 3D Models with volume Rendering

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of how neural radiation fields work.

2. Gain the ability to use neural radiation fields to achieve novel view synthesis.

3. Gain the ability to optimize the neural radiation field based on experimental phenomena and results.

Project summary：

This program focuses on giving students an understanding of what a neural radiation field is and how to train it well.

Assessment：

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. Interns should be motivated to learn and be able to overcome difficulties.

3. The interns should have an interest in the field of deep learning and 3D reconstruction and have already demonstrated strong research potential.

4. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly computer programming languages python, linear algebra and deep learning.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#3 Prof. Kai Wan’s research team (Positions: 1-2)

Introduction to the project (lab):

Cloud computing is the fastest growing type of computing, with many companies leveraging it to provide services that users can access through web browsers. Cloud computing utilizes three service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). A key feature of cloud computing is its elasticity, which allows for the dynamic allocation of computing and storage resources based on demand. As a result, cloud service providers can implement a pay-as-you-go pricing strategy.

Coded Elastic Computing (CEC) is a framework designed to address elasticity events. Elasticity events refer to situations during distributed iterative learning tasks where certain nodes may leave or join, and the occurrence of these events is not known in advance. CEC can dynamically allocate computational loads among all nodes and, inspired by coded computation, can detect and correct transmission errors. Moreover, heterogeneous CEC is capable of handling different computational speeds and storage sizes.The main objective of this project is to improve existing heterogeneous CEC algorithms using coding techniques while considering practical factors such as encoding and decoding complexity, heterogeneous and unstable computation speeds, and limited coding fields. Additionally, the project aims to build a small distributed computing system to experiment with and demonstrate the superiority of the proposed CEC algorithms over traditional uncoded non-elastic computing solutions.

Prof. Wan is a member of "Mobile Communication and Intelligent System Laboratory". This laboratory is at the forefront of interdisciplinary research in intelligent wireless communication and artificial intelligence. Our primary research areas include integrated sensing and communication systems, with a focus on random matrix theory, information theory, and coding theory. We are particularly focused on the development of intelligent reflecting surfaces and AI-assisted wireless communication systems. Additionally, we are pioneering work in distributed integrated sensing and communication systems, aiming to revolutionize communication networks with enhanced efficiency and adaptability. In the realm of artificial intelligence, our research extends to foundational theories and methods, including AI explainability, multi-modal foundational large models, and AI model lightweighting technologies. By advancing these areas, we aim to push the boundaries of AI and wireless communication systems, creating novel solutions for future intelligent networks and applications.

Project Aims:

This internship project aims to:

1. Learn the fundamentals of cloud computing, including network architecture and Coded Elastic Computing.

2. Become familiar with network coding schemes and computational resource allocation.

3. Understand mobile edge computing, including end-edge-cloud coordinated frameworks and communication-efficient schemes.

Methods:

1. Literature review.

2. For heterogeneous elastic computing structures, we utilize reinforcement learning to determine the performance of elastic nodes.

3. Based on the learned statistical performance, we apply a combinatorial optimization strategy to achieve the optimal task allocation scheme.

4. For different learning networks, we design specific task allocation strategies using Tencent Cloud.

5.Under finite fields, we derive the information-theoretic lower bound to validate the optimal solution.

Learning outcomes:

By the end of this project interns should have gained:

1. Deep understanding of the technology of cloud computing and elastic computing.

2. Learning the classical network coding scheme and combinatorial optimization strategy.

3. Implement elastic computing strategies in real cloud systems.

Project summary:

This project aims to utilize the mathematical tools of network coding, based on the fundamental theories of machine learning, to deeply explore the performance of common neural networks within an elastic framework. By integrating mathematical theory and machine learning knowledge, the project conducts formula derivation and experimental analysis on unresolved issues in heterogeneous networks, such as node departure and entry, communication lag, and over-smoothing.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of cloud computing and have already demonstrated strong research potential.

3. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly Matrix theory and Probability Theory.

4. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#4 Prof. Caiming Qiu’s research team (Positions: 1-2)

Introduction to the project (lab):

Current research indicates that AI outperforms traditional methods in complex communication tasks, including wireless environment modeling, signal detection, channel estimation, beamforming, positioning, mobility management, wireless resource allocation, traffic prediction, network state tracking, and so on.

In recent years, AI has also demonstrated remarkable capabilities in joint optimization across multiple modules. Studies on end-to-end intelligent transceivers have shown that jointly optimizing transmitter-side modulation with receiver-side channel estimation, equalization, and demodulation can further enhance the transmission performance of communication systems.

Prof. Qiu is the leader of the "Mobile Communication and Intelligent System Laboratory". This laboratory is at the forefront of interdisciplinary research in intelligent wireless communication and artificial intelligence. Our primary research areas include integrated sensing and communication systems, with a focus on random matrix theory, information theory, and coding theory. We are particularly focused on the development of intelligent reflecting surfaces and AI-assisted wireless communication systems. Additionally, we are pioneering work in distributed integrated sensing and communication systems, aiming to revolutionize communication networks with enhanced efficiency and adaptability. In the realm of artificial intelligence, our research extends to foundational theories and methods, including AI explainability, multi-modal foundational large models, and AI model lightweighting technologies. By advancing these areas, we aim to push the boundaries of AI and wireless communication systems, creating novel solutions for future intelligent networks and applications.

Project Aims:

This project aims to extend the concept of end-to-end intelligent transceivers to multi-antenna systems by

1. Jointly designing transmitter-side modulation, layer mapping, and precoding with receiver-side channel estimation, signal detection, and demodulation.

2. An AI-based transceiver link will be developed to evaluate the transmission performance of MIMO end-to-end intelligent transceivers.

Methods:

1. Performance Evaluation and Gain Source Analysis of MIMO End-to-End Intelligent Transceivers;

2. Generalization Performance Evaluation of MIMO End-to-End Intelligent Transceivers

Learning outcomes:

By the end of this project interns should have gained:

1. A schematic overview about the 5GNR up/down link;

2. Learning to use the end-to-end training for transceiver link;

3. Knowledge about the theoretical analysis and transceiver scheme design.

Project summary:

The objective of this program is to provide students with an understanding of the methodology for utilizing the AI tool for the design of 5GNR up/down linkages.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. Interns should be motivated to learn and be able to overcome difficulties.

3. The interns should have an interest in the field of deep learning and 5G/6G radio access network (RAN) and have already demonstrated strong research potential.

4. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly computer programming languages python, linear algebra and deep learning.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#5 Prof. Zhenyu Liao’s research team (Positions: 1-2)

Introduction to the project (lab):

In-context memorization and learning of high-dimensional temporally correlated data with linear Transformer

1. Project Background

This project focuses on investigating the core theoretical interest in modern machine learning—the high-dimensional statistical properties of linear attention.

Unlike standard feed-forward neural networks, Transformer-type models exploit the attention mechanism to mix input data adaptively for better memorization, generalization, and reasoning. This project aims to evaluate the in-context memorization capacity of nonlinear attention using random matrix theory, analyzing it as a function of the interplay between model weights, input statistics, dimension ratios, and nonlinearity.

2. Introduction of the Lab

The team is the "Mobile Communication and Intelligent System Laboratory" of the School of Telecommunications, Huazhong University of Science and Technology, led by Professor Qiu Caiming.

The team focuses on intelligent reflector, integrated network and intelligent communication, artificial intelligence theory and technology, He has published more than 30 articles in IEEE Transactions on Information Theory, NeurIPS, ICML, ICLR and other flagship conferences and journals in the field of communication and artificial intelligence, and published a monograph in Cambridge University Press. Consolidate the mathematical foundation of the new generation of mobile communication and intelligent systems, promote the cross-development of information science and mathematics, and promote the future wide application in thousands of industries such as short-range communication, unmanned driving, intelligent transportation, and edge computing.

Project Aims:

1. Investigate and establish a theoretical framework for the high-dimensional statistical properties of linear attention.

2. Analyze the in-context memorization capacity of linear attention, especially under high-dimensional conditions.

3. Provide numerical results to demonstrate the effectiveness of the proposed asymptotic analysis.

Methods:

1. Introduction of Deterministic Equivalents: Describe the high-dimensional behavior of the random spectral matrix.

2. High-dimensional Linearization of Attention Kernel: Linearize the attention kernel matrix while preserving its high-dimensional characteristics.

3. Numerical Experiments: Conduct numerical experiments to show the effectiveness of the proposed asymptotic analysis methods for moderately large sample sizes and feature dimensions.

Learning outcomes:

By the end of this program, participants should have gained:

1. Research ability to the core theoretical problems in modern machine learning, and in particular an understanding of the contextual learning, memory, generalization, and reasoning capabilities of Transformer.

2. Learning utilizes stochastic matrix theory to analyze and evaluate the performance of key components in machine learning models.

3. Numerical experiment skills on real data sets, including the ability to design experiments, analyze results to verify the validity of theoretical analyses, and draw conclusions from them.

Project summary:

This project aims to explore and solve the theoretical challenges of linear attention mechanisms in high-dimensional data. By applying stochastic matrix theory, we can not only gain a deeper understanding of how linear transformer works, but also evaluate its performance in context memory tasks. The project demonstration verifies the validity of theoretical analysis through numerical experiments, which provides a solid foundation for future research and application. The research results of this project are applicable to multiple fields, and by improving the speed and accuracy of data analysis, we are paving the way for smarter and more effective decision support systems.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in undergraduate interns.

2. Interns should be motivated to learn and able to overcome difficulties.

3. The intern should be interested in the field of machine learning and communication and have demonstrated strong research potential.

4. In addition, they should have learned the basics of a range of areas in science and technology, especially linear algebra, probability theory, and deep learning.

5. English and Chinese are our teaching and working languages, regardless of whether the intern knows Chinese or not.

#6 Prof. Zenan Ling’s research team (Positions: 1-2)

Introduction to the project (lab):

This project focuses on utilizing diffusion models for image editing, specifically driven by textual and visual prompts. Diffusion models have shown significant advancements in image generation, providing high-quality results while overcoming issues like mode collapse, common in traditional generative adversarial networks (GANs). By incorporating textual and visual prompts, users can manipulate images with greater flexibility and precision. This project aims to explore how these models can be guided by user input—both in the form of text and visual cues—to generate tailored image edits, such as modifying image content, adjusting styles, or enhancing specific elements. The goal is to develop a system that leverages these prompts to facilitate intuitive and effective image editing tasks.

Prof. Ling is a member of the "Mobile Communication and Intelligent System Laboratory". This laboratory is at the forefront of interdisciplinary research in intelligent wireless communication and artificial intelligence. Our primary research areas include integrated sensing and communication systems, with a focus on random matrix theory, information theory, and coding theory. We are particularly focused on the development of intelligent reflecting surfaces and AI-assisted wireless communication systems. Additionally, we are pioneering work in distributed integrated sensing and communication systems, aiming to revolutionize communication networks with enhanced efficiency and adaptability. In the realm of artificial intelligence, our research extends to foundational theories and methods, including AI explainability, multi-modal foundational large models, and AI model lightweighting technologies. By advancing these areas, we aim to push the boundaries of AI and wireless communication systems, creating novel solutions for future intelligent networks and applications.

Project Aims:

The main objectives of this internship project are:

1. To understand the fundamentals of diffusion models and their application in image generation and editing.

2. To investigate how textual and visual prompts can be integrated into the image editing process, enabling users to guide and control the generated outputs.

3. To enhance the flexibility and precision of image editing tasks by optimizing the model's ability to respond to various prompts.

Methods:

1. Literature Review: Review existing research on diffusion models and image editing techniques with textual and visual prompts.

2. Model Development and Training: Develop and train a diffusion model to process textual and visual prompts for image editing tasks.

3. Optimization and Evaluation: Optimize the model and evaluate its performance in generating accurate image edits based on prompts.

Learning outcomes:

1. Understand the fundamentals of diffusion models and their application in image editing.

2. Gain hands-on experience in integrating textual and visual prompts for image manipulation.

3. Develop skills in optimizing and evaluating generative models for practical image editing tasks.

Project summary:

This project focuses on developing a diffusion model for image editing driven by textual and visual prompts, enabling flexible and precise modifications to images. Interns will work on model development, training, optimization, and evaluation to improve prompt-based image generation.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are looking for undergraduate interns with an interest in deep learning and generative models.

2. Interns should be eager to learn and able to work independently on research tasks.

3. A basic understanding of image processing and neural networks, especially in relation to generative models, is preferred.

4. Interns should have experience with programming in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.

5. It is not necessary for interns to understand Chinese, as English is the primary language of instruction and work.

#7 Dr. Ke Xian’s research team (Positions: 1-2)

Introduction to the project (lab):

Digital Human Project

The Digital Human Project is an innovative initiative that explores the cutting-edge technology of digital humans within the context of the metaverse. Our focus is on creating highly realistic and interactive digital representations of humans that can be integrated into virtual environments. This project is grounded in the latest advancements in artificial intelligence, machine learning, and computer graphics, aiming to push the boundaries of what's possible in virtual characters and their interactions.

Prof. Xian is a member of the "Mobile Communication and Intelligent System Laboratory". This laboratory is at the forefront of interdisciplinary research in intelligent wireless communication and artificial intelligence. Our primary research areas include integrated sensing and communication systems, with a focus on random matrix theory, information theory, and coding theory. We are particularly focused on the development of intelligent reflecting surfaces and AI-assisted wireless communication systems. Additionally, we are pioneering work in distributed integrated sensing and communication systems, aiming to revolutionize communication networks with enhanced efficiency and adaptability. In the realm of artificial intelligence, our research extends to foundational theories and methods, including AI explainability, multi-modal foundational large models, and AI model lightweighting technologies. By advancing these areas, we aim to push the boundaries of AI and wireless communication systems, creating novel solutions for future intelligent networks and applications.

Project Aims:

The primary objectives of this internship project are to:

1. Develop a digital human model that can be used in various applications within the metaverse.

2. Optimize the performance of the digital human model to ensure realistic and efficient rendering.

3. Enhance the interactivity and responsiveness of digital humans to user inputs.

Methods:

1. Model Training: Utilizing state-of-the-art deep learning techniques to train digital human models that can mimic human behavior and appearance.

2. Behavioral Simulation: Implementing algorithms that allow digital humans to respond to user interactions in a natural and lifelike manner.

3. Rendering Techniques: Employing advanced rendering technologies to ensure that digital humans are visually indistinguishable from real humans.

Learning outcomes:

By the end of this project, interns will have acquired:

1. In-depth knowledge of digital human creation and the technologies that support it.

2. Practical skills in training and optimizing digital human models for various applications.

Project summary：

This program is designed to provide students with a comprehensive understanding of digital human technology and how to effectively train and deploy these models within the metaverse.

Assessment：

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. Interns should be motivated to learn and be able to overcome difficulties.

3. The interns should have an interest in the field of deep learning and digital human and have already demonstrated strong research potential.

4. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly computer programming languages python, linear algebra and deep learning.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#8 Prof. Ge Xiaohu’s research team (Positions: 1-2)

Introduction to the project (lab):

1. Background to the project

To reduce the energy consumption of communication systems, circuits based on transistors are facing the challenge of low energy consumption for signal processing. Some typical technologies, such as shortening the transistor size, reducing the number of electrons and lowering the supply voltage are widely used by present circuits to achieve low energy consumption. However, as the gate size of transistors is getting closer to the mesoscopic scale, how to model and analyze the non-equilibrium information processing of transistors is an essential challenge for transistor circuits.

2. The content of the project is the modeling of mesoscale transistor circuits and the exploration of interesting phenomena.

Project Aims:

This internship project aims to:

1. Learning modeling methods for mesoscale transistor circuits

2. Learning how to look at the circuit behavior from a new perspective

Methods:

1. Literature review

2. Modeling of transistor circuits based on stochastic thermodynamics

3. Learning optimization methods of circuits

Learning outcomes:

By the end of this project, interns should have gained:

1. Understanding the effect of scale on circuits

2. Learning the modeling and optimization methods of transistor circuits

Project summary:

This program focuses on giving students an understanding of circuit behavior at the mesoscopic scale.

Assessment:

Oral presentation is required by the end of the internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. Interns should be motivated to learn and be able to overcome difficulties.

3. In addition, they should have studied the basic knowledge about a range of fields in science and technology, such as MATLAB, Python, and principles of analog electronic circuits and digital electronic circuits.

4. Although this project involves intersections with the field of physics, a theoretical foundation in physics is not essential compared to interest.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

③Wuhan National High Magnetic Field Center

#1 Prof. Han Xiaotao’s research team (Positions: 1-2)

Analysis and Correction of the Impact of Temporal Variability of Pulsed Magnetic Fields on Nuclear Magnetic Resonance Measurements

Introduction to the project:

Ever since discovered in the 1940s, nuclear magnetic resonance (NMR) has manifested itself as one of the most significant tools to derive detailed information on the atomic scale about material properties. Nowadays, the trend of performing NMR experiments in the high magnetic field is actively driven not only by higher sensitivity and resolution, but also by the desirable exploration of field-induced physics in modern material science. Currently, only pulsed resistive magnets are practically available to produce a high magnetic field exceeding 46 T and even up to 100 T for timescales typically in the range of 1–100 ms. However, the time-varying nature of the pulsed magnetic field causes the violent fluctuation of the resonance frequency and leads to the distortion of the NMR spectra, which requires in-depth analysis and development of the algorithm for signal correction.

The content of the project is learning the basic principles of NMR, numerical analysis and signal processing algorithms.

Project Aims:

The internship project aims to investigate the effect of pulsed magnetic field temporal variability on NMR signals and to develop a corresponding signal correction algorithm and program.

Methods:

1. Modeling and theoretical analysis.

2. Numerical analysis.

3. Algorithmic programming.

Learning outcomes:

By the end of this project interns should have gained:

1. A deep understanding of NMR.

2. Knowledge of modeling and numerical analysis.

3. The ability to program using MATLAB or LabVIEW.

Project summary:

This project is designed for those who want to gain experience in advanced skills of modeling, numerical analysis, and algorithmic programming.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of instrumentation and measurement and have already demonstrated strong research potential.

3. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly circuit theory, electromagnetic fields, signals and systems.

4. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#2 Prof. Han Xiaotao’s research team (Positions: 1-2)

Optimized design of highly sensitive magnetization detection coils for de Haas quantum oscillation experiments

Introduction to the project (lab):

1. Background of the project.

When a magnetic field is applied to a metal magnet, de Haas-van Alphen oscillations with period 1/B are observed. This effect is often detected at very low temperatures and in sufficient high magnetic fields, which can be measured by the superconducting quantum interference device (SQUID) with a high sensitivity (~10-7emu). However, magnetization measurement in pulsed magnetic fields adopts a conventional induction method different from SQUID. This technique has a relatively low sensitivity, fabrication of a high density (>1000 turns) pick-up coil within a small sample space (DF~3mm) is challenging.

As shown in figure 1(a), a pulsed magnetic field up to 55 T with a duration time of 10 ms is generated by using a short-pulsed magnet energized by 1.25 MJ capacitor bank. The signals of dM/dt from the sample and the signals of dB/dt from external magnetic field are collected and integrated as a function of magnetic fields. Fourier transform of the data should be done finally to analyze the frequency information of the de Haas-van Alphen oscillations.

The pick-up coil is the key for magnetization measurement in pulsed magnetic fields. The coil contains part A and part B which are well compensated in applied external fields with opposite winding directions. Figure 1(b) shows several designs of the pick-up coil at the WHMFC. Attention also needs to be paid to the coil model shown in figure 2 to address the signal oscillations associated with large inductors, as shown in figure 3.

2. The content of project is learning to wind magnetization detection coils and optimize their frequency characteristics, in addition to performing pulsed-discharge tests to study the phenomenon of quantum oscillations.

Fig. 1. Magnetization measurement system in pulsed magnetic fields (a) and three different designs of the pick-up coil (b).

Fig. 2. Circuit model of a magnetization measurement system.

Fig. 3. Signal oscillations of large-turn coils during pulse testing.

Project Aims:

1. In this project, we will optimize design of the pick-up coil, and assemble high density with winding more turns in a limited sample space.

2. Solve the problem of signal oscillation due to large inductance in large turn coils.

3. Through this work, we expect to realize an accurate measurement of de Haas-van Alphen quantum oscillations under pulsed high magnetic fields.

Methods:

1. Winding high sensitivity magnetizing coils.

2. Frequency characteristics analysis and spectrum optimization of pick-up coils.

3. Pulse discharge test and data analysis.

Learning outcomes:

By the end of this project interns should have gained:

1. Mastery of high-sensitivity magnetizing coil winding techniques and sample rod fabrication for cryogenic experiments.

2. Understanding large-turn coil circuit models and their frequency characteristics optimization methods.

3. Pulse discharge experiment operation method.

Project summary:

This project focuses on a new experimental technique, i.e., de Haas-van Alphen measurement in pulsed magnetic fields using a high sensitivity pick-up coil. By utilizing this technique, we are able to measure the magnetic quantum oscillations of metallic sample at low temperatures and extend magnetic field range much higher than that by SQUID.

Assessment:

Oral presentation within 30 minutes is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the fields of condensed matter physics, low temperature physics, as well as experimental techniques.

3. In addition, they should have studied the basic knowledge about the cryogenics and are able to operate liquid helium and liquid nitrogen.

4. Skills of origin and LabVIEW software are also needed.

5. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our language of instruction and work.

#3 Prof. Gang Xu’s research team (Position: 1)

Introduction to the project (lab):

Background of the project.

This project will focus on the first principles calculations of topological materials.

Topological materials are a novel class of quantum materials characterized by the bulk topological invariants in their band structure and unique edge modes. For instance, three-dimensional topological insulators are defined by the Z2 invariants and exhibit a surface gapless Dirac cone, while two-dimensional chiral topological superconductors are identified by the superconducting Chern number and chiral Majorana edge modes. These intriguing edge modes lead to new physical phenomena and prospective applications, including low-power electronics and quantum computing. Therefore, the quest for effective topological materials remains a long-term goal in the field of condensed matter physics.

The contents of this project are listed as follows:

1. Learn about Density Functional Theory (DFT) and how to do DFT calculation.

2. Learn about the simulation packages, including VASP, Wannier90, Wanniertools.

3. Use the packages to calculate the band structures and topological invariants of topological materials, including topological insulators, topological semimetals and topological superconductors.

Project Aims:

The internship project aims to gain the knowledges of computational condensed matter physics and calculate the band structures and topological invariants of materials.

Methods:

1. Density Functional Theory.

2. VASP, Wannier90, Wanniertools.

3. Calculations of band structures and topological invariants.

Learning outcomes:

By the end of this project interns should have gained:

1. Knowledge of Density Functional Theory.

2. Knowledge of topological materials.

3. The ability to calculate the band structures and topological properties.

Project summary:

This project is designed for those who want to gain the experience in the computational condensed matter physics, and the knowledge of topological physics.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in the undergraduate interns.

2. The interns should have an interest in the field of computational condensed matter physics and should have studied the basic knowledge about a range of fields in science and technology, particularly condense matter physics, quantum materials.

3. The working language can be English for non-Chinese speakers.

④ Wuhan National Laboratory for Optoelectronics

#1 Prof. TANG Jiang’s research team (Positions: 1)

Introduction to the project (lab):

Study on High Efficiency Tandem Perovskite Solar Cells

Extensive research on perovskite-based photovoltaics (PV) over the past decade led to rapid development, with power conversion efficiencies (PCEs) exceeding 25.2% being realized. Hybrid organic–inorganic metal halide perovskite semiconductors continue to attract enormous attention due to their exceptional optoelectronic properties, such as their high absorption coefficients, high carrier mobilities, and low recombination rates. The widely tunable band gap of these perovskites by compositional variations of the halide anion in the perovskite crystal structure allows strong light absorption in a broad spectral range. With their low material costs and a wide range of possible deposition techniques, perovskites qualify as promising candidates for next-generation multi-junction PV. Moreover, combined with established PV technologies, like wafer-based silicon or copper indium gallium selenide films, perovskite-based tandems are currently the most promising technology for terrestrial PV to enable PCEs exceeding the single-junction Shockley-Queisser limit.

The recent development in perovskite PV has been largely underpinned by advances in the composition and morphology of the perovskite absorber layer as well as progress in device architectures by employing passivation layers and optimizing hole and electron transport layers (HTLs and ETLs). Nevertheless, fundamental challenges, such as the toxicity of lead-based perovskites, the limited stabilities of the various layers-such as the perovskite absorber, charge trans-port layers (CTL), and combinations thereof-in terms of moisture, light, and heat stress remain to be solved. One promising route to cope the stability of the devices is via the replacement of organic CTLs with inorganic counterparts. In this regard, the HTLs, copper iodide (CuI), copper thiocyanate (CuSNC), and nickel oxide (NiOx) have been shown to promise good intrinsic chemical stability compared to the commonly used organic HTL spiro-OMeTAD. In addition, ETLs like zinc oxide (ZnO), tin oxide (SnO2), and mesoporous titanium dioxide (TiO2) are known to be intrinsically stable and have been demonstrated to result in highly efficient and stable perovskite solar cells.

However, conventional methods for layer deposition significantly limits the choice of materials, deposition techniques, and device architectures, due to solvent incompatibilities or process-induced damage of underlying layers during vacuum-based physical vapor deposition (PVD) and chemical vapor deposition (CVD) processes. Whereas the CTL under the perovskite must be robust against the perovskite deposition, many polar solvents have to be avoided for the layers, which are solution-processed on top of the perovskite to prevent decomposition or degradation. Similarly, high temperatures, radicals, and ion bombardment must be limited due to the possible damage of the underlying layers during PVD and CVD techniques such as sputtering, atomic layer deposition, or electron-beam evaporation. In this regard, metal-oxide or fullerene-based buffer layers are usually employed that protect the underlying absorber layer. Therefore, not every combination of ETL and HTL is accessible in a straightforward manner.

In this project, we will vigorously develop the technology of laminated perovskite solar cells.

Introduction of the Lab

Our research group, Optoelectronic Devices and 3D Integration Team (ODTI), was founded in May 2012, mainly focusing on the research of new photoelectric conversion materials and devices, hoping to do the basic research with new ideas in science and promising technology, in order to live up to the taxpayers’ money and the time of students and teachers. The research group attaches importance to students’ basic scientific research skills, emphasizes all-round development, advocates student-oriented management philosophy, and strives to create a happy and comfortable environment for scientific research and experiment. We sincerely invite students with passion and pursuit for scientific research to join us. Those with photoelectric physics and materials chemistry background are welcome.

The leader of ODTI is Professor Tang Jiang, Dean of the School of Optics and Electronic Information, Huazhong University of Science and Technology, and Deputy Director of Wuhan National Laboratory for Optoelectronics. The team has 9 professors, 5 associate professors, and nearly 200 post-doctoral, doctoral and master students. Relying on advantageous scientific research platforms such as Wuhan National Laboratory for Optoelectronics, School of Optics and Electronic Information, and Hubei Optics Valley Laboratory, our team is mainly engaged in the application of optoelectronic materials, devices and chips. Some research achievements have been made in antimony selenide thin film solar cells, quantum dot near infrared detectors, perovskite X-ray detectors and light-emitting diodes. The related work is published in Nature, Nature Photonics, Nature Energy, Nature Electronics and other journals.

Visit ODTI at: http://tfsc.wnlo.hust.edu.cn/

Wuhan National Laboratory for Optoelectronics (WNLO) is one of the six national research centers approved by the Ministry of Science and Technology of China in 2017. As an interdisciplinary research center, WNLO focuses on basic scientific and technological researches in the fields of optoelectronics for information, energy, and life science. In recent years, it has achieved fruitful results in fields including brain imaging, solar cells, ultrafast lasers, laser manufacturing, optoelectronic devices and integration, data storage etc.

Visit WNLO at: http://english.wnlo.hust.edu.cn/

Project Aims:

In this project, in view of the good photovoltaic performance of the perovskite, the perovskite thin films with narrow band gap (band gap width around 1.15 eV) and wide band gap (band gap width around 1.70 eV) are prepared by rotating coating method, thermal evaporation method and scraping method, respectively. The open-circuit voltage and current of the laminated cell are increased by additive doping and energy level regulation. The intermediate interconnect layer ITO is prepared by Reactive Plasma Deposition (RPD) technology, which effectively achieves efficient carrier tunneling compound and improves the device's filling factor and efficiency. Strive to achieve efficiency over 30%, MPP of the T50 performance stability over 1000 hours of laminated solar cells.

Methods:

1. Preparation of wide and narrow band gap perovskite thin films

Based on the structure of ABX3, the band gap of the perovskite material can be adjusted to 1.15 eV and 1.70 eV, respectively, by adjusting the components of the A cation and the X halogen ion. Perovskite films with stable performance and suitable band gap can be prepared by means of spinning coating, thermal evaporation and scraping coating.

2. High efficiency and stability of single perovskite solar cell preparation

Efficient and stable single perovskite solar cells with band gaps of 1.15eV and 1.70eV are prepared respectively to obtain large short circuit current and open circuit voltage, and strive to achieve efficiency of more than 20%, laying a foundation for the preparation of laminate cells.

3. Preparation of perovskite and perovskite laminated cells

(1) Study on interconnect layer between perovskite cell and perovskite cell. RPD and ALD technologies are used to prepare the intermediate junction layer, and the charge loss and transmission process at the intermediate junction layer are studied to obtain the maximum efficiency.

(2) Preparation of perovskite battery and perovskite battery integrated device. Optimized structure design and device fabrication of laminated cells.

Project summary:

In this project, we regulate the band gap of perovskite materials by adjusting its A-site cation and X-site halogen based on the CH₃NH₃PbI₃ perovskite ion respectively. Perovskite films with band gaps of 1.15 eV and 1.70 eV are prepared by rotating coating, thermal evaporation and scraping coating. Based on these thin films, a single perovskite solar cell with a band gap of 1.15eV and 1.70eV is prepared, respectively. The high short-circuit current and open-circuit voltage are obtained, and the efficiency is strived to exceed 20%, which lays a foundation for the preparation of laminated cells. RPD and ALD technologies are used to prepare the intermediate junction layer, and the two types of perovskite solar cells aree integrated to prepare the layered perovskite solar cells. The charge loss and charge transfer at the intermediate junction layer are studied, and the efficiency of the prepared integrated devices is more than 26%.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in undergraduate interns.

2. The interns should have an interest in the field of big data analysis, solar cell, optoelectronic device, and have already demonstrated strong research potential. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly image and date processing, preparation and characterization of semiconductor materials, SEM, TEM, XRD measurement, etc.

3. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our languages of instruction and work.

#2 Prof. LU Peixiang’s research team (Positions: 1)

Introduction to the project (lab):

Generation and Measurement of Attosecond Laser

Attosecond science has emerged as an important research area of ultrafast phenomena within the past decade. Owing to its numerous successes, attosecond science has created important new knowledge of the fundamental science of the interaction between electrons and photons. Recently, some key research topics have been identified, which are expected to make major breakthroughs for the next attosecond frontiers, such as the MHz high-repetition high-order harmonic frequency combs for the joint frontier of precision spectroscopy and ultrafast science, attosecond-pump/attosecond-probe experiments for observing and controlling electronic processes in atomic and molecular physics, and nonlinear science occurring on the attosecond time scale.

To seriously tackle the above interesting research topics, one of the most important issues is the development of high-power isolated attosecond pulses (IAPs) and/or attosecond pulse trains (APTs). IAPs and APTs are produced using high-order harmonic generation (HHG) in gases. To date, high-power APTs have successfully been generated owing to research on high harmonic energy scaling using a loose-focusing geometry. APTs with sufficiently high energy in the microjoule range with a high conversion efficiency on the 10−4 level were generated, which are intense enough for implementing some applications, such as nonlinear attosecond optics without the assistance of laser pulses, single-shot femtosecond holography and an external injector for a seeded free-electron laser (FEL) in the extreme-ultraviolet (XUV) region. In contrast, the output energy of IAPs is still not sufficient for various applications, although the shortest pulse duration achieved is sub-100 as. The widespread application of IAPs has been limited because of the low photon flux and the complexity of the laser systems required to produce IAPs. At present, the production, characterization, and application of high-energy IAPs are still under active investigation.

Theoretically, synthesized sub-cycle driver pulses with a “perfect waveform” were proposed for optimized HHG and IAP production, and the enhancement of the HHG by using these synthesized waveforms was experimentally and theoretically studied. On the basis of the above results, synthesized few/multicycle driving pulses are expected to enable scaling up of the energy and efficiency of IAP generation. To obtain IAPs with pulse energies reaching the microjoule level, a Terawatt (TW)-class waveform synthesizer is necessary, which requires the use of high-energy femtosecond lasers that are all operated at low repetition rates (e.g., 10 Hz). In this project, we will set up a Terawatt (TW)-class two-color laser waveform synthesizer to generate a high-power IAP. We expect the pulse duration of the IAP to be about 200 as and the pulse energy can reach about 300 nJ.

Introduction of the Lab:

Ultrafast Optics (UFO) Lab in HUST mainly engages in the studies of strong field ultrafast optics. The research interests include attosecond pulse generation, atomic, molecular, and optical physics in strong-field, and ultrafast micronano optics. In the past twenty years, the UFO Lab has achieved many fruitful research results. The group members have published nearly 600 SCI papers in PRL, Nature Photonics, Nature Communications, Optics Letters, Optics Express, PRA, and other academic journals. The UFO Lab has 15 in-service teachers and good experimental conditions, which can guarantee the successful implementation of this project.

Visit UFO at: http://ufolab.phys.hust.edu.cn/

Wuhan National Laboratory for Optoelectronics (WNLO) is one of the six national research centers approved by the Ministry of Science and Technology of China in 2017. As an interdisciplinary research center, WNLO focuses on basic scientific and technological researches in the fields of optoelectronics for information, energy, and life science. In recent years, it has achieved fruitful results in fields including brain imaging, solar cells, ultrafast lasers, laser manufacturing, optoelectronic devices and integration, data storage etc.

Visit WNLO at: http://english.wnlo.hust.edu.cn/

Project Aims:

In this project, we aim to produce a high-power IAP (200 as, 300 nJ) with a TW-class two-/three-color laser waveform synthesizer and measure its spatio-temporal profile with an all-optical method.

Methods:

For the generation of high-power IAP, we will build a TW-class waveform synthesizer for two- or three-color laser fields. The synthesized laser field will interact with rare gases (e.g., Ar, Ne, Xe, and so on) to generate high-order harmonics. We will optimize the synthesized laser waveform to optimize the power and duration of the generated IAP.

To precisely manipulate the waveform of the synthesized laser, we will build a stability-control system for the synthesizer. In this system, we have to eliminate the relative timing and phase jitters as well as stabilizing carrier-envelope phases (CEPs) for each channel in the synthesizer.

For the measurement of the generated IAP, we will build an attosecond streaking camera equipment. Meanwhile, we will also develop an all-optical method to measure the pulse duration of the attosecond laser.

Learning outcomes:

By the end of this project interns should have gained:

1. The construction of a multi-channel (two-color, three-color) femtosecond laser coherent synthesizer and its stability-control system.

2. The construction of attosecond streaking camera equipment.

3. The all-optical measurement of attosecond laser.

Project summary:

In this project, we intend to carry out experimental research on the generation and measurement of high-power attosecond laser. In this project, we will use a terawatt femtosecond laser in combination with a multi-channel laser coherent synthesis scheme to interact with rare gases to generate high-power attosecond pulses. And the generated attosecond laser will be measured and characterized by the attosecond streaking camera technology or the all-optical method.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in undergraduate interns.

2. The interns should have studied basic knowledge about a range of fields in science and technology, particularly quantum mechanics, electrodynamics, optics, and signal processing.

3. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our languages of instruction and work.

#3 Prof. Wang Jian’s research team (Position: 1)

Introduction to the project (lab):

Research on Silicon based Multi-Dimensional Multiplexing Optical Communication System

In recent years, with the rapid development of modern information technology, data communication traffic has also shown an explosive growth, which puts forward higher requirements for data storage, interconnect communication and signal processing. Using photons as information carriers in optical communications could take the unique advantages of high speed, large bandwidth and low latency, while silicon integrated photonics features the characteristics of high integration, low power consumption and low cost, providing a promising platform for the development of modern communication networks. Besides, photons could offer multiple physical resource dimensions such as frequency/wavelength, polarization, time, complex amplitude, and spatial structure as shown in Fig. 1, which could be developed into various multiplexing technologies as well as hybrid multi-dimensional multiplexing technology, providing the possibility for the further improvement of communication capacity. Therefore, the research on silicon integrated photonic chips for multi-dimensional multiplexing and processing has important strategic significance for the development of future high-speed and large-capacity communication networks.

Although multiple multiplexing technologies such as wavelength-division multiplexing (WDM), polarization-division multiplexing (PDM), mode-division multiplexing (MDM) and advanced modulation formats have developed rapidly in the past few decades, challenge remains to handle hybrid multi-dimensional multiplexing signals applied in fiber-to-chip optical processing systems. Not only the multi-dimensional signal processing puts forward higher requirements to the performance of on-chip integrated devices, including bandwidth limitation, polarization sensitivity and so on.

However, challenges remain to realize multidimensional hybrid multiplexing data transmission and signal processing between few-mode fiber transmission links and on-chip optical processing networks, since the optical coupling between fiber and chip almost exclusively in single mode regime.

Introduction of the Lab:

The Multi-Dimensional Photonics Laboratory (MDPL) is led by Professor Wang Jian. The group is mainly focusing on the multiple physical resource dimensions of photons (frequency, time, polarization, complex amplitude, space), especially the new dimension, i.e., space dimension of photon. The research interests include: high-speed and large-capacity vortex beam/vector beam/structured light multi-dimensional optical communication, data center and high-performance optical interconnection, free space/underwater/optical fiber/chip optical communication and optical interconnection, high-speed and large-capacity intelligent multi-dimensional optical signal processing, space division multiplexing optical amplification technology, manipulation of multi-dimensional optical field on chip, silicon-based photonic integrated chip, optoelectronic integration chip, femtosecond laser direct writing photonic integrated chip, surface plasma/metamaterial/metasurface devices, special transmission fiber/active fiber/optical fiber devices.

Prof. Wang Jian’s research interests include optical communications, optical signal processing, silicon photonics, photonic integration, orbital angular momentum, and structured light. He is a fellow of Optica, SPIE, and IEEE. He has been the Principal Investigator of >20 earmarked grants (NSFC, 973, etc.). He has published over 240 refereed international journal papers on Science, Science Advances, Nature Photonics, Nature Communications, Light: Science & Applications, Physical Review Letters, Optica, Laser & Photonics Reviews, ACS Photonics, Nanoscale, Nanophotonics, Photonics Research, Optics Express, Optics Letters, etc. He has authored and co-authored over 150 international conference papers on OFC, ECOC, CLEO, etc. He has also given over 110 tutorial/keynote/invited talks in international conferences including the invited talk at OFC2014 and tutorial talk at OFC2016.

Wang Jian has done pioneer works in twisted light communications employing orbital angular momentum (OAM) multiplexing, which facilitates sustainable capacity increase of optical communications. Wang Jian and co-workers demonstrated terabit-scale free-space OAM communications (Nature Photonics, 6, 488, 2012, times cited: 2608; Science, 337, 655, 2012). Wang Jian has later played a leading role in twisted and structured light communications as well as multi-dimensional optical communications. He studied fundamental properties of spin angular momentum (SAM), OAM and vector beams (Nature Communications, 12, 4186, 2021; Physical Review Letters, 127, 233901, 2021; Laser & Photonics Reviews, 11, 1700183, 2017, Cover; Laser & Photonics Reviews, 14, 2000249, 2020, Cover), implemented outdoor free-space and adaptive underwater OAM communications, demonstrated OAM amplifier (Research, 2020, 7623751, 2020) and record 300-km fiber OAM communications. He realized structured light communications (Bessel, Airy, vector) and demonstrated the direct fiber vector eigenmode multiplexing transmission (Light: Science & Applications, 7, 17148, 2018, Cover). He authored/co-authored important review articles and comments on twisted (Photonics Research, 4, B14, 2016, times cited: 378; Advances in Optics and Photonics, 7, 66, 2015, times cited: 927) and structured light communications (Nature Photonics, 12, 249, 2018). These works were interviewed by the Nature Photonics editor (Dr. Rachel Won) (Nature Photonics, 11, 619, 2017).

Wang Jian and co-works have made an important breakthrough in multi-dimensional entanglement transport through single-mode fiber (SMF) (Science Advances, 6, eaay0837, 2020). By entangling spin-orbit degrees of freedom of a biphoton pair, passing the spin photon down the SMF while accessing multiple OAM subspaces with the other, multi-dimensional entanglement transport was realized, showing distinct advantages of deployment over legacy networks with conventional SMF.

Wang Jian has done innovative works in photonic integrated devices on different platforms for twisting and structuring light. Wang Jian demonstrated on-chip OAM generator (Optics Letters, 43, 3140, 2018, Editor’s Pick), all-fiber OAM generator (Optics Letters, 40, 4376, 2015), metasurface for structuring light (Optics Letters, 38, 932, 2013), and integrated vector laser (ACS Photonics, 6, 3261, 2019, Cover). Very recently, he demonstrated ultra-compact broadband polarization diversity OAM generator with 3.6x3.6 μm2 footprint (Science Advances, 5, eaau9593, 2019).

Wang Jian has also done lots of works of great significance in chip-scale optical signal processing. Some representative contributions include terabit-scale on-chip optical interconnects, chiral silicon photonic circuits (Optica, 6, 61066, 2019), subwavelength slot waveguides (Physical Review Letters, 127, 233902, 2021), subwavelength grating slot microring resonators (Nanoscale, 12, 15620, 2020), reconfigurable multi-functional photonic signal processor (ACS Photonics, 7, 1235, 2020, Cover) and programmable multi-task photonic signal processor (ACS Photonics, 7, 2658, 2020, Cover) on a silicon chip.

Wuhan National Laboratory for Optoelectronics (WNLO) is one of the six national research centers approved by the Ministry of Science and Technology of China in 2017. As an interdisciplinary research center, WNLO focuses on basic scientific and technological researches in the fields of optoelectronics for information, energy, and life science. In recent years, it has achieved fruitful results in fields including brain imaging, solar cells, ultrafast lasers, laser manufacturing, optoelectronic devices and integration, data storage etc.

Visit WNLO at: http://english.wnlo.hust.edu.cn/

Project Aims:

In this project, we will propose to use silicon-based integrated photonic devices to realize multi-dimensional fiber-to-chip optical processing system for hybrid wavelength-, mode- and polarization-division multiplexing signals.

First of all, in order to solve the few-mode fiber transmission link to the silicon integrated optical processing chip, the efficient mode coupling device between few-mode fiber and multi-mode photonic chip needs to be designed. We propose a multi-mode coupler based on heterogeneous waveguides, which enables the direct coupling for multiple guided modes between FMF and silicon integrated chip. The proposed multi-mode coupler consists of tapered FMF and silicon integrated multistage waveguide tapers buried in the polymer waveguide, where the tapered fiber acts as one transition optical waveguide to reduce the mode spot size as well as the coupling loss, while the silicon integrated multistage waveguide tapers are used to realize mode conversion.

Secondly, the multi-dimensional multiplexing processing on the silicon-based chip needs to be designed. The silicon multiple guided modes are demultiplexed into fundamental TE and TM modes based on the cascaded ADC structure, then PR structure is introduced to rotate the fundamental TM mode into fundamental TE mode. The silicon integrated chip takes advantages of parallel cascaded micro-ring resonator array to perform the signal processing function as reconfigurable optical add-drop multiplexer (ROADM). Finally, the chip can realize multi-dimensional fiber-to-chip optical processing system for hybrid wavelength-, mode- and polarization-division multiplexing signals.

Methods:

This project mainly adopts FDTD and EME algorithms for simulation and design of silicon-based integrated photonic devices: the efficient mode coupling device between few-mode fiber and multi-mode photonic chip, mode (de)multiplexer, micro-ring (wavelength (de)multiplexer) and polarization (de)multiplexer so as to build the whole silicon based processing chip. For the fabrication of devices, we will use electron beam lithography (EBL) technology to fabricate silicon-based integrated photonic devices.

Project summary:

After decades of development, the traditional single-mode optical fiber communication has approached its Shannon capacity limit and have begun to have a new capacity crisis. Photons offer multiple physical resource dimensions such as polarization, frequency/wavelength, amplitude, phase, time, etc. The exploration of new dimensions and multi-dimensional integration of photonics is the key to the sustainable expansion of optical communications. Silicon-based integrated photonics has the characteristics of high integration, low power consumption and low cost, which provides a promising platform for the development of optical communication networks. This project studies the key technical issues in multi-dimension multiplexing processing, and realizes the hybrid wavelength-, mode- and polarization-division multiplexing on chip with photonic integrated devices, and realizes the multi-dimension signal processing system of fiber-to-chip- to-fiber.

Assessment:

Oral presentation is required by the end of internship.

Applicant profile:

1. We are interested in undergraduate interns.

2. The interns should have an interest in the field of optical communication, optical processing, and integrated optics. In addition, they should have studied the basic knowledge about a range of fields in science and technology, particularly optical communication, optical processing, and integrated optics.

3. It is no matter whether the interns understand Chinese or not, English as well as Chinese are our languages of instruction and work.