The aim of the project is to develop advanced neural control systems for complex adaptive behaviors of multi-limbed robotic systems. The ultimate goal is to enable the robotic systems to perform complex tasks, e.g., locomotion in complex environments with autonomous learning, action planning, and decision-making capabilities.
Task1: The applicant will develop high-level neural control for autonomous lifelong learning, action planning, and decision-making of limbed robots.
Task2: The applicant will develop adaptive neuromechanical control with sensory feedback and online adaptation for stable and efficient climbing strategies of a gecko-like robot.
The applicant will be expected to have experience in some of the following topics:
- Machine learning for robotics (e.g., reinforcement learning, deep learning),
- Computational neuroscience,
- Embodied artificial intelligence,
- Neural control, learning, and plasticity
- Bio-inspired robotics,
- Locomotion control,
- Walking/climbing robots,
Additionally, the applicant should have good programming skills (e.g., C, C++), excellent writing skills, and be able to work independently. Familiarity with tools such as ROS, robot simulation (V-REP), TensorFlow, MatLab, and Git is desirable.
The successful applicant for the position will be affiliated to Institute of Bio-inspired Structure and Surface Engineering (IBSS) at Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Degrees/desired profile: Master/PhD in Artificial Intelligence/Machine Learning, Robotics, Electrical Engineering and Computer Science, Computational Neuroscience, or an equivalent area.
An application must be in English and include:
A covering letter explaining his/her approach to the Task1 or 2
Certificates (Bachelor and Master's degree certificates for PhD position, and including PhD's degree certificate for Postdoc position)
At most three articles illustrating his/her publication record
List of publications indicating the publications attached