Open research topics with highlight
The research topics in this page are open to international applicants and keep updating. If you are interested in joinning a certain research group, please contact the supervisor or the contact person under each topic. You can also find a full list of PhD supervisors here.
1. Smart sensor systems and machine learning for health informatics
3. Developing innovative ultrasound method for hard and soft tissues
4. Superresolution in-situ measurement of micro/nano structures of large-scale components
5. Micro-LED display, visible light communication, and underwater optical wireless communication
6. Artificial synaptic devices for neuromorphic computing
7. Selective growth of organic functional materials
9.1 SoC design for embedded intelligence
9.2 Industrial Internet of Things
12. Digital Twins for Intelligent Manufacturing based on Industrial Robot
13. Angular normalization of land surface temperature derived from satellite data
14. Light-head deep learning techniques study for computer vision analysis
15. Excitonic behaviors in two-dimensional materials
16. Fully integrated wearable sensor arrays and SoC for in situ perspiration analysis
1. Smart sensor systems and machine learning for health informatics
Supervisor: Prof. CHEN Wei Email: w_chen@fudan.edu.cn
Many challenges exit in health monitoring and management, such as continuous, accurate, and comfortable monitoring of multi-parameters, early detection and warning of diseases, as well as the interaction with environments. The development of smart sensors, Internet of Things, advanced materials, machine learning and data fusion technology has inspired the innovation on intelligent designs for healthcare. Physiological signs, behaviors and environmental information can be obtained effectively. By jointly analyzing physiological behavior parameters with environmental interaction information and using data fusion technology, the health-related activities can be identified. Personal health monitoring and forecasting will be provided assisting to develop personal healthcare plan, and guide people towards a healthier lifestyle. The multi-disciplinary research on smart sensing system, Internet of Things, machine learning, and biomedical signal processing will bring new development for improving the quality of life for people ranging from babies to aging population and have long term social impacts.
Special requirement for applicants:
a. Major background: electronic engineering, biomedical engineering, signal processing, microelectronics, telecommunication engineering, computer science and engineering;
b. Strong research skill; relevant scientific papers or patents have been published or submitted;
c. Professional, Responsible, Good teamwork and communication skills;
d. Self-motivated and creative;
e. Have relevant research experience in machine learning, signal processing, smart sensor systems, sleep monitoring, or brain activity monitoring;
f. Strong writing skills in English (and or) Chinese.
2. AI in medical image analysis, Machine learning for medical application, Medical image process, Ultrasound imaging
Supervisor: Prof. YU Jinhua Email: jhyu@fudan.edu.cn
The research direction of Prof. Jinhua Yu's group is the development and application of medical image big data, and research on a new generation of intelligent information processing technology based on machine learning, artificial neural network and complex data mining. The purpose is to reveal the intrinsic relationship between medical image big data and the occurrence, development and prognosis of diseases, and finally to establish models of precise diagnosis, treatment plan selection and prognosis prediction based on intelligent algorithms.
Special requirement for applicants:
Open to PhD and Master application. Applicants must have a strong interest and passion for medical image processing and artificial intelligence. Undergraduate or master's degree majors in electrical engineering, information engineering, biomedical engineering, mathematics, etc.
3. Developing innovative ultrasound method for hard and soft tissues
Supervisors: Prof. XU Kailiang and Prof. TA De'an Email: xukl@fudan.edu.cn, tda@fudan.edu.cn
Intelligent medical ultrasound lab is seeking candidates to work on developing innovative ultrasound method for hard and soft tissues. The topics include:
1）Ultrasonic guided waves based long cortical evaluation
2）Ultrasonic bone elasticity imaging
3）Transcranial ultrasound focusing and imaging
4）Ultrasonic backscattering imaging for cancellous bone
5）Low-intensity pulse ultrasound stimulation for fracture repair
6）Photoacoustic bone imaging
7）Ultrasound imaging and medical imaging processing
Intelligent medical ultrasound lab, created by Academician Weiqi Wang, has focused in the biomedical ultrasound since 1970s. Currently, the lab consists of several professors, PhD/Master students and postdoctoral researchers. Over the years, the lab has successfully undertaken many national key projects and National Science Foundation projects with outstanding contributions in the field.
The student will join the group headed by Prof. Dean Ta (http://www.buee.fudan.edu.cn). The candidates will acquire deep knowledge in the field of biomechanics, biomedical ultrasound and biomedical engineering which allows him to think about research problems in a versatile way and strengthen his scientific career development.
Those interdisciplinary projects will help the applicant acquire the expertise of simulation tools, such as the finite-difference time-domain (FDTD) computation and finite element (FE) modelling, animal experiments using a programmable ultrasound phase-array system, ultrasound signal processing, ultrasound imaging, and medical image processing etc.
Special requirement for applicants:
Applicants with a bachelor degree in biomedical engineering, electronic engineering, applied physics, medical physics or other closely related areas will be preferred. Programming skills (Matlab and C) are required. The candidates must have demonstrated the ability to perform experimental work, to publish papers in English, and to work independently and collaboratively.
Candidates are invited to send their application to Prof. Kailiang Xu (xukl@fudan.edu.cn) and Prof. Dean Ta (tda@fudan.edu.cn), including CV, letter of motivation, publication list (if there is), and contact information for at least 2 references (reference letters are not required at this time).
4. Superresolution in-situ measurement of micro/nano structures of large-scale components
Supervisor: Prof. ZHANG Xiangchao and JIANG Xiangqian Email: zxchao@fudan.edu.cn
Large-scale components like imprinting rollers contain complex micro/nano structures, and such trans-scale measurement is a challenging problem in precision engineering. Digital holographic measurement is powerful on measuring complex surfaces and compensating wavefront distortions caused by the micro-scale structured features. The whole component is measured by scanning the measured head, and the sub-aperture images are fused together to obtain the measured data of the whole area. Multiscale geometric transform is applied based on the theory of compressed sensing. Intrinsic signals associated with the measured features are identified in the transform domain, consequently the blurring and distortions resulting from the CCD subsampling, environmental noise, imaging aberrations etc can be addressed. Single-shot wavefront reconstruction is also developed to realize continuous measurement without phase shifting. This project is of great significance for the fast in-situ measurement and intelligent manufacturing of precision components.
Special requirement for applicants:
Bachelor/Master on optical engineering, computer science or applied mathematics
5. micro-LED display, visible light communication, and underwater optical wireless communication
Supervisor: Prof. TIAN Pengfei and GU Erdan Email: pftian@fudan.edu.cn
We have been working in the multidisciplinary research area of semiconductor devices and related systems of next-generation display technique of micro-LED display and high-speed optical communication.
Special requirement for applicants:
PhD or Master applicants with strong self-motivation of research are encouraged to send an e-mail to pftian@fudan.edu.cn.
6. Artificial synaptic devices for neuromorphic computing
Supervisor: Prof. XIONG Shisheng Email: sxiong@fudan.edu.cn
Contact person: LI Dongxue Contact email: 15110720055@fudan.edu.cn
With the explosive developing of artificial intelligence (AI) and the internet of things (IOT), it is imperative to explore novel electronic devices operating with low power consumption to meet the increasing demand of edge computing. Since the current computers, based on Von Neumann architecture, execute program instructions sequentially, there is a bottleneck existing between the processing unit and the memory. To reduce the huge power consumption that caused during data shuttling, one way is to create a brain-like intelligent system starting from the hardware level. For instance, memristors, namely the fourth basic element of circuit devices, can mimic the function of the neural synapses with the capability of memory, association and learning. The research focus in our group lands on the fabrication of ultra-large scale memristor cross-bar arrays, along with the integration with CMOS for highly efficient pattern recognition tasks in the field of AI. The applications include computer vision, natural language processing, robotics, and so on. Students participating the project will get intensive training on nanofabrication, electronic characterization and realization of machine learning algorithms on memristive devices.
Special requirement for applicants:
Majoring in microelectronics and solid-state electronics, material science and engineering, physics and related disciplines, PhD and Master (both).
7. Selective growth of organic functional materials
Supervisors: Prof. LIU Ran Email: rliu@fudan.edu.cn
This topic will deposit small-molecule organic conducting, semiconducting or ferroelectric materials on pre-patterned micro/nano structures for fabrication of optoelectronic devices. The dynamic processes of molecular diffusion on different surfaces during selective growth will be studied and the device integration on flexible substrates explored.
Special requirement for applicants:
PhD or Master applicants with strong self-motivation of research.
Supervisors: Prof. HU Laigui and Prof. LIU Ran Email: laiguihu@fudan.edu.cn
The topic inlcudes: 1) Organic functional materials and thin film devices; 2) Organic ferroelectric materials and thin film devices.
Special requirement for applicants:
Open to PhD/Master applicants. Master applicants must pass HSK-5 and above.
Background: Electronics; Physics; Chemical Physics and other related majors
9. Topic 1: SoC design for embedded intelligence
Circuits and systems for edge intelligence, including DNN accelerators, neuromorphic architecture, and wireless perception.
Topic 2: Industrial Internet of Things
Edge and fog computing for industrial Internet of Things, mainly focus on reliable and timing-critical applications.
Supervisor: Prof. ZOU Zhuo Email: zhuo@fudan.edu.cn
Special requirement for applicants:
Multiple vancancies avalable at PhD and Master levels. Candiates are welcomed with background in EE/CS, information and communciation technologies, preferably with knowledge/experience on digital IC design, VHDL/Verilog, and embedded programming.
11. Study on Advanced Wireless Communication Technologies for 5th and forthcoming 6th Generation (5G/6G) Wireless Systems
Supervisor: Prof. ZHOU Xiaolin and Prof. WANG Xin Email: zhouxiaolin@fudan.edu.cn
With the continuous progress and development of communication technology, 5G wireless communication technology has become a common topic in the communication industry. The research covers the key technologies for 5G and upcoming 6G wireless systems, which includes: (1) Artificial Intelligence (AI) in Wireless Systems, (2) Massive-MIMO Scheme, (3) Non-Orthogonal Multiple Access (NOMA) for Future Radio Access, (4) Heterogeneous Cloud Communication, etc.
Special requirement for applicants:
We are looking for a self-motivated and enthusiastic researcher, with an interest in theoretical research and experimental work. You are required to have a Bachelor degree in a relevant engineering or related subject, such as Information and Communication Systems, Signal Processing, Engineering Mathematics.
Open to PhD and Master Applicants.
12. Digital Twins for Intelligent Manufacturing based on Industrial Robot
Supervisor: Prof. KONG Lingbao Email: LKong@fudan.edu.cn
Intelligent Manufacturing plays the substantial role in modern industries. The technologies include Artificial Intelligence (AI), Machine Learning, Smart Optical Vision, Optical Sensors, and so on. Industrial robot has various advantages such as large motion space, more freedom, easy to control, and low cost, which make it as a unique motion mechanics for modern manufacturing including machining process and metrology. Digital twins are one of the most import issues and technologies in intelligent manufacturing, and provide a dual-direction strategy, i.e., mirror from physical system to cyber system, and control from cyber system to physical system. In this research, digital twins for intelligent manufacturing based on industrial robot will be investigated, some key applications include optical vision system based on industrial robot for precision inspection and measurement, and flexible machining such as polishing, additive manufacturing, etc.
Special requirement for applicants:
Applicants should have research background and interest in optical precision engineering, machining learning algorithms, and other related areas. Both Master and PhD candidates are welcome for application.
13. Angular normalization of land surface temperature derived from satellite data
Supervisor: Prof. JIANG Geng-Ming Email: jianggm@fudan.edu.cn
Aiming at the inconsistent problem of land surface temperature (LST) derived from different satellite data, this project will conduct the study on the angular normalization of satellite-derived LST. First, radiative transfer modelling experiments will be carried out, and an empirical angular normalization model will be proposed according to the relationship between the simulations and viewing angles for non-isothermal pixel. Then, inspired by the development of the bi-directional reflectance distribution function (BRDF) model, we try to develop a kernel-driven and semi-empirical angular normalization model of LST. Finally, based on the temporal normalization model, a new algorithm will be proposed to retrieve directional LST from multi-source and multi-angle satellite data. The new algorithm will be applied to directional LST retrieval from combined polar-orbiting and geostationary satellite data.
Special requirement for applicants:
Open to both PhD and Master applicants
14. Light-head deep learning techniques study for computer vision analysis
Supervisor: Prof. CHEN Tao Email: eetchen@fudan.edu.cn
The Light-head Deep Learning and Visual Analysis focuses on new CNN architecture studies for object detection and classification, under the conditions of weakly supervised and computing resource constrained scenarios. The scopes of vision analysis include: semantic classification of images and videos, target detection and segmentation; analysis or reconstruction of video scenes; tracking of key targets and other common visual applications. Typical application scenarios include: target monitoring based on pedestrians or faces , traffic management and planning based on vehicle detection and attribute analysis.
Special requirement for applicants:
We are open to both PhD and Master applicants, who have shown great interest in deep learning, machine leaning, computer vision, video and image analysis, and embedded AI algorithm study. Students from following majors are warmly welcome:
Computer engineering, software engineering, electronic engineering, signal processing, applied mathematics, statistical analysis
15. Excitonic behaviors in two-dimensional materials
Supervisor: Prof. ZHANG Hao Email: zhangh@fudan.edu.cn
Two-dimensional (2D) materials are strategic materials for future applications in microelectronics, optoelectronics and etc. In 2D materials, the the electronic and dielectric responses to incident light are different from their bulk counterpart, since the dimensionality reduction results in the weakening of dielectric screening. Therefore, many-body effects such as electronic excited states and electron-hole couplings are required to investigated on top of ground states. Here, the ab initio many-body perturbative theory and Bethe-Salpeter Equation will be used to study the excitonic behaviors in some 2D materials. Then the phonon-assisted absoprtion and relaxation of excitons through phonons will be theoretically studied for the subsequent experimental comparisons. We will also try to design some novel devices based on excitons in 2D materials.
16. Fully integrated wearable sensor arrays and SoC for in situ perspiration analysis
Supervisor: Prof. QIN Yajie and Prof. ZHENG Lirong Email: yajieqin@fudan.edu.cn
Human sweat is rich in physiological information, hence, skin-like wearable sensors are expected to sample human sweat for continuously monitoring a person’s state of health. One of the on-going project is to develop the mechanically flexible sensor arrays, which simultaneously measure respiration metabolities and electrolytes. To enable the whole sensing system be more flexible and skin-friendly, single chip solution is preferred for the wearable electronics, which integrated circuits for the sensor signal conditioning, processing, wireless transmission and wireless powering. To enable the low power implementation for such complex SoC, low power analog and mixed-signal integrated circuit design technologies have to be studied.
Special requirement for applicants:
1) Major background of microelectronics or electronics are required;
2) With the experience and knowledge on analog integrated circuits and digital integrated circuits.
3) Open to PhD and Master applicants.
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