Wissenschaftliche/r Mitarbeiter/in im Bereich "Data-driven control for nonlinear systems" (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
Designing control laws based on a model that is obtained by first principles or is identified using input/output data is commonly known as model-based control. An inherent limitation of model-based control is, thus, that it requires a system model for the development of control laws. However, due to the complex nature of recent control systems, it is not always possible to derive a model using first principles or system identification algorithms. With that in mind, data-driven methods, which skip the intermediate step of modeling the system, to investigate control systems have been proposed and they have become a topic of increasing interest in recent times. However, most of these developments are limited to linear systems.
Task Description
In terms of this position, we are looking for someone who can perform exploratory research on data-driven control for classes of nonlinear systems. In particular,
- study the structural properties such as controllability and observability of systems based on measurements;
- design state/output feedback control laws such that the closed-loop system satisfies certain desired properties, for example, stability;
- develop data-driven methods for input or state estimation;
- collaborate closely with academic researchers who are specialized in one or more areas such as control theory, machine learning, and applied mathematics.
Qualification
- Above-average master's degree in applied mathematics or control engineering
- Knowledge of at least one programming language: Matlab or Python
- Proficiency in English
- Highly motivated and 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
data-driven control
nonlinear systems
optimization
system identification
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