Wissenschaftliche/r Mitarbeiter/in im Bereich "Optimal Transport in Control and Machine Learning" (m/w/d)
About us
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.
Research Framework
Optimal transport (OT) is a mathematical framework for finding the most efficient way to move a mass distribution from one location to another. It is based on the idea of minimizing the cost of transporting the mass, where the cost can be measured in terms of distance, time, or some other metric. OT has a long history, dating back to the 18th century when it was first introduced by the mathematician Gaspard Monge. However, it has only become widely studied in recent years due to the advances in computational optimization and the growing interest in OT from other fields such as machine learning, biology, and economics. Unbalanced OT (UOT) is a generalization of OT that allows for the possibility that the total mass of the two distributions may not be the same. This can be useful in situations where we want to transport mass from one distribution to another, but we may not be able to transport all of the mass from one distribution to the other. UOT is typically formulated as a constrained optimization problem. The goal is to minimize the cost of transporting mass from one distribution to the other, subject to the constraint that the total mass of the two distributions is preserved.
Task Description
The research compiles from the following list of tasks.
- Developing novel optimization schemes for solving OT and UOT problems keeping in mind the aspects of computational efficiency and scalability.
- Compare the above developed schemes with the state of the art methods
- Developing machine learning methods for solving OT and UOT problems.
- Develop connections to PDEs and to consider relevant classes of PDEs for which OT formulation can be obtained. It is specifically important to consider the case of (fully) nonlinear PDE.
- Collaborating closely with academic researchers who are specialized in one or more areas such as control, machine learning and PDEs.
- Applying OT formulation to design feedback controllers, robust controllers, optimal control laws.
- Apply the above developed schemes to specific problems in the domains of Biology, Processes engineering and Autonomous driving.
Qualification
- Above average university degree in mathematics and optimization
- Knowledge of at least one programming language: Matlab, Python, C++ is expected
- Knowledge in dynamical systems and PDEs
- Proficiency in English or / and German is essential
- Highly motivated, eager to work within a team or independently.
We offer
- 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.
Contact
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
Keywords
Optimal control
Optimization
Deep learning
Statistical learning
PDEs
Application Papers
Cover Letter
CV
University Certificates
References
List of Publications
Application Deadline
15. April 2024
We will process your application as soon as received.
Job Availability
Immediate