Position Overview

KU is seeking a highly motivated Post-doctoral Fellow to work on performing cutting-edge research in autonomous vehicles (AVs) with background in Theoretical Computer Science. The funded project includes two objectives: (O1) explore a class of novel, practically driven generalizations of classical routing and planning problems, such as TSP and UFP, with application to navigation and mission planning of autonomous vehicles. One such problem is the TSP problem with drones and legged robots for last-mile logistics applications or environmental surveillance. In tackling these new problems, we seek to devise efficient routing algorithms with certifiable worst-case performance guarantees. (O2) Developing a theoretical framework to synthesize multi-agent controllers and coordination schemes that are robust against nondeterminism due to parameter uncertainty and disturbances. In view of real-world nuances and sensing errors, we aim at developing robust planning and scheduling algorithms that tolerate noisy parameters. To ensure timely and high-grade decision-making, we seek to design polynomial-time approximation algorithms that offer formal definite bounds on sub-optimality or probably approximately correct (PAC)-style guarantees.

As part of the project, the researcher will have access to state-of-the-art research equipment and facilities at the Autonomous Vehicles Lab and will get an opportunity to work alongside researchers and graduate students from various backgrounds as part of a wider team.

Position Requirements

• Technical hands on experience and knowledge in the field of interest, optimization and some background in robust optimization.
• Solid programming skills
• Optional: background in Machine learning
• Optional: background in robotics
• Adhere to the University\'s information security and confidentiality policies and procedures, and report breaches or other security risks accordingly
• Perform any other tasks assigned by the Line Manager

Times Higher Education