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Job Description
One Research Engineer 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 Engineer (will) will work closely with the Principal Investigator (PI) on reinforcement learning control and learning-based control, which leverage the advantages of both deep learning and conventional controls with theoretical guarantees. The RE will develop AI-assisted planning and control algorithms that enable intelligent and robust autonomous operations and multi-agent collaborations. Hardware experiments will be carried out to test and demonstrate the applications of the developed algorithms.
The initial appointment duration is 12 months, which can then be extended based on an evaluation at the end of the initial appointment.
The research project involves (1) theoretical research in control, learning, and optimization, (2) developing new control and learning algorithms, and (3) 3D Simulations and real hardware experiments.
The candidates should have a Bachelor’s or Master’s degree from a reputable university, with expertise in control theory, reinforcement learning theory, and aerial robotics.
A successful candidate should have a solid mathematical background (such as in calculus, linear algebra, ODE/PDE, optimization, real analysis, probability theory, stochastic process, etc). Strong publication records in leading journals and conferences, and practical hands-on experience in applying reinforcement learning to real robotics applications (e.g., autonomous driving and unmanned aerial vehicles) will be a big plus.
Qualifications
• Possess a bachelor’s or master’s degree in either Electrical Engineering or strictly related (e.g., Mathematics, Computer, Communication, Mechanical, or Information Engineering).
• Have research experiences in control, reinforcement learning, distributed optimization, multi-agent, autonomous driving, and/or UAVs.
• Possess a strong academic record proved through coursework (especially math-intensive courses) and projects during his/her undergraduate and master’s studies.
• Proficient in C++ or Python. Familiar with machine-learning tools and packages. Familiar with various 3D simulation environments for quadrotor simulations. Familiar with ROS and quadrotor control algorithm.
• Have well-established analytical and problem-solving skills, as documented by publications that are relevant to the field of control theory and robotics applications, reinforcement learning control for robotics applications.
• Excellent communication skills as he/she is required to publish and present results at conferences and journals independently.
• Activity performed in world-class research environments is highly valued.
