The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.

https://www.mv.uni-kl.de/mec/home.
 

While autonomous driving has been an extremely hot topic of research lately, most of it looks at cars as local, independent agents. Although this allows to imitate the behaviour of a human driver, this does not exploit the fast intercommunication capacities networked computers have. Using this advantage to ensure synchronised trajectories amongst cars allows for safer, faster, and more fuel-efficient experience overall, and is the obvious next step for fully autonomous driving. In this project, we are interested specifically in synchronising multiple autonomous vehicles located at a traffic light, with the goal of easing passage for a given vehicle (e.g. an ambulance). While the challenges here are obviously similar to the ones in autonomous driving, an extra care is taken with regards to computation complexity and delay, as joint optimization is more complex and the result has to be distributed to all agents.
 

The research compiles from the following list of tasks.

• Review the literature in the field of optimal control, MPC, planning, multi-agent planning
• Expand your knowledge of techniques related to planning and scheduling (graph search/A* algorithm, MPC, integer programming, mixed-integer programming...)
• Model multi-agent systems, including non-deterministic obstacles and other stochastic elements
• Design and implement algorithms for solving the multi-agent planning problem described above
• Test your algorithm on vehicles and verify its properties: soft real-time capability, optimality, completeness, etc.
• Publish your results to top-conferences
   

• University degree in mathematics, electrical engineering, computer science, or any related field
• Technical skills in control engineering, optimization
• Experienced with formulation and solution of problems in nonlinear optimization, mixed-integer programming
• Knowledge of differential equations, dynamical systems
• Highly motivated, eager to work within a team or independently
• Familiarity with Python, C++
   

• Payment according to TV-L E13 with an initial one-year time limit
• The possibility to do a PhD and to teach is given in case of scientific aptitude
• TUK strongly encourages qualified female academics to apply
• Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
• Electronic application is preferred. Please attach only one coherent PDF.

You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.

Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax:  +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de

Autonomous driving
Planning
Multi-agent system
Model precictive control
 

Cover Letter
CV
University Certificates
References
List of Publications

31. October 2023

Immediate