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Job Description
A 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 work closely with the Principal Investigator (PI) on quadrotor path planning, control, SLAM, swarm path planning, and cooperative transport. The approach will leverage the advantages of both deep reinforcement learning and conventional controls, combining model-based and model-free methods. Strong hardware experience and C++ coding especially related to quadrotor path planning and control are required.
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 should have a Bachelor’s or Master’s degree from a reputable university, and with strong hardware expertise in aerial robotics.
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, and practical hands-on experience in applying reinforcement learning to real robotics applications (e.g., autonomous driving and unmanned aerial vehicles) are highly valued.
Qualifications
• Possess a bachelor’s or master’s degree in either Electrical Engineering or strictly related (e.g., Mathematics, Computer, Electrical, Mechanical, Information Engineering).
• Have research experiences in control, reinforcement learning, distributed optimization, multi-agent, UAVs.
• Possess a strong experience in practical hands-on hardware project, familiar with 3D modelling and mechanical design. 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.
• Open to Fixed Term Contract
