Scientific Co-Worker (f/m/d) for Hybrid Models of Dynamic Systems Using Machine Learning and Expert Engineering Knowledge
In the Faculty of Computer Science, Electrical Engineering and Mathematics at the Department of Automated Control there is a vacancy for the position of a
Scientific Co-Worker (f/m/d) for Hybrid Models of Dynamic Systems Using Machine Learning and Expert Engineering Knowledge
(Salary Level E 13 TV-L)
with 100% of the regular working hours starting earliest from 2022-09-01. The position is initially lim-ited until 2025-08-31 due to third-party funding in accordance with the federal state Science Employ-ment Law (WissZeitVG). The contract period corresponds to the approved project funding. The pos-sibility of a doctorate or post-doc qualification is given.
Project description and responsibilities:
• Research on hybrid (state-space) modelling of dynamic systems using both data-driven ma-chine learning techniques as well as expert-based a priori knowledge
• Research on optimal system excitation patterns in order to retrieve information-rich data sets in a short amount of time while satisfying system constraints (model-free and model-based explo-ration strategies facing uncertainty)
• Transfer of simulation-based pre-investigations to real-world experiments using embedded con-trol hardware together with IoT edge computing platforms
• Testing of the proposed methods using real-world electrical power conversion systems (e.g., power electronics and drives) and mechatronic systems (e.g., inverted pendulum, drones) in the laboratory
• Scientific exchange and active cooperation with related research groups
• Contributing to open-source software repositories addressing the above topics
• Writing scientific papers for journals and conferences
Your qualifications:
• Very good university degree (master, Ph.D. or similar) in the field of control engineering, elec-trical engineering, mechatronics, computer science or similar
• Profound knowledge of optimal control of dynamic systems using model-based and/or model-free approaches (model predictive control, reinforcement learning,…)
• Profound knowledge of dynamic system models derived from first principles and/or from empir-ical data sets (machine learning, system identification)
• Profound knowledge of software-related engineering tools and programming languages (e.g., Python, Julia, Matlab/Simulink, dSPACE, VHDL, C/C++,…)
• Desirable: practical experience in working at laboratory test benches for embedded systems (using microprocessors, FPGAs or rapid-control-prototyping hardware)
• Independent and team-oriented approach to work
• Very good command of written and spoken English or German
Applications from women are expressly welcome and will be given preference in accordance with the LGG in the event of equal suitability, qualifications and professional performance, unless reasons relating to the person of a competitor prevail. Part-time employment is generally possible. The appli-cation of suitable severely disabled persons and persons with equal rights within the meaning of Book IX of the German Social Law (SGB IX) is also welcome.
Applications with complete documents (cover letter, CV, references in a single PDF-file) should be send via e-mail under the reference number 5354 to:
Information regarding the processing of your personal data can be located at: https://www.uni-pader-born.de/zv/personaldatenschutz.
Dr.-Ing. Oliver Wallscheid
Paderborn University
Warburger Str. 100
33098 Paderborn, Germany
oliver.wallscheid@upb.de
