Research Associate in "Optimal Transport in Control and Machine Learning" (m/w/d)

Reference number: WM FB MV 1493

Term limitation:

Kaiserslautern

Scope:

Full time

Remuneration:

Entgeltgruppe 13 TV-L

Department:

Mechanical and Process Engineering

With about 17,000 students, more than 300 professorships and around 160 degree programs, the University of Kaiserslautern- Landau (RPTU) is the technical university of the state of Rhineland-Palatinate. As a place of top international research, it offers excellent working conditions and career opportunities. Those who study, research or work at RPTU experience a cosmopolitan environment and shape the future.

Optimal transport (OT) is a mathematical and optimization framework for finding the most efficient way to move a mass distribution from one location to another.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, control, biology, and economics.

Your area of responsibility:

The research compiles from the following list of tasks.

• Investigate mathematical foundations of OT, in particular with regard to the PDE formalism
• 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 problems.

Your requirements profile:

• 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 nonlinear (and possibly) 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.
• Above average university degree in mathematics, control and optimization
• Knowledge in dynamical systems and PDEs
• Knowledge of at least one programming language: Matlab, Python, C++ is expected
• Proficiency in English or / and German is essential
• Highly motivated, eager to work within a team or independently.