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Job Description
Research Fellow position is open in the research group of Assistant Professor Zhao Lin, at the Department of Electrical and Computer Engineering, National University of Singapore (NUS).
The Research Fellow will work closely with the Principal Investigator (PI) on theories and applications of learning-based control. The project will investigate and develop both deep reinforcement learning algorithms and conventional robust adaptive control methods, to develop safety-critical learning-based control algorithms that leverage the advantages of both model-based and model-free methods. In particular, the developed algorithms will be demonstrated through robust control of drones.
The initial appointment duration is 12 months, which can then be extended based on an evaluation at the end of the initial appointment.
The candidates will be responsible for conducting theoretical research and algorithm development. The candidates are expected to help the PI supervise junior PhD students as well.
The candidates should have a Ph.D. degree from a reputable university, with expertise in robust adaptive control, reinforcement learning theory, collision-avoidance trajectory optimization, etc.
A successful candidate should have a solid mathematical background (such as in calculus, linear algebra, optimization, etc.). Strong publication records in leading journals and conferences are required.
Qualifications
• Possess a Ph.D. Degree in either Electrical Engineering or strictly related (e.g., Mathematics, Computer, Communication, Mechanical, or Information Engineering).
• Have substantial research experiences in robust adaptive control, reinforcement learning, learning-based control, model predictive control, etc.
• Possess a strong academic record proved through coursework (especially math-intensive courses) and projects during his/her undergraduate and doctoral studies.
• Strong publication records in leading journals and conferences are highly valued
• Excellent communication skills as he/she is required to publish and present results at conferences and journals independently.
• Have well-established analytical and problem-solving skills, as documented by publications that are relevant to the field of reinforcement learning and control for robotics applications.
• Activity performed in world-class research environments is highly valued.
• Open to Fixed Term Contract.
