Research Assistant
vor 5 Stunden
The University of Kassel is a modern and growing University with round about students. The university's wide range of expertise covers subjects in the natural, applied, cultural and social sciences.
At the University of Kassel, Department Elekrotechnik/Informatik, Institute for System Analytics and Control – Automation and Sensorics in Networked Systems Group (Prof. Dr.-Ing. Christian A. Hans), the following position is available as of 01 January 2026:
Research Assistant (m/f/d), EG 13 TV-H, fixed term-positionlimited to three years, full-time (currently 40 hours per week)Application deadline:
Date of hire:
vacancy number:
38887
The position is part of the research project "Hierarchical Control of Multiscale Systems for the Case of Resilient Future Electrical Power Systems" and is initially limited to three years in accordance with § 2 WissZeitVG. The opportunity to pursue a doctoral degree (PhD) is provided.
At the Automation and Sensorics in Networked Systems (ASN) Group, methods from automatic control, artificial intelligence, and networked systems are combined. A major application focus of our work lies in complex energy systems with a high share of decentralized, uncertain, and renewable generation. Our goal is to enable future low-carbon energy systems that support sustainable economies.
Within the new research project "Hierarchical Control of Multiscale Systems for the Case of Resilient Future Electrical Power Systems", methods for the modelling, analysis, and control of large-scale, physically coupled systems with nonlinear dynamics and multiple time scales will be investigated. The objective is to develop hierarchical control approaches that consistently account for interactions between different time scales and subsystems, thereby ensuring stable and resilient operation under varying conditions and disturbances. As a motivating application, the project focuses on future electrical power systems with a high share of renewable energy sources, which are characterized by reduced inertia and increased uncertainty.
A particular emphasis of the ASN group's activities in the project lies in the development and implementation of distributed model predictive control (MPC) methods that ensure robustness in dynamically changing environments. The work includes both theoretical contributions in the field of control of networked systems and practical work on implementing the developed concepts in resilient energy systems. The project is carried out in close interaction with the Control and Systems Theory Group at the University of Kassel and in collaboration with international academic partners.
Tasks:
- Independent research activities within the project, focusing on hierarchical and distributed control of networked dynamical systems, in particular:
- Modelling and analysis of complex nonlinear systems with an emphasis on future energy systems (e.g., using LPV models and abstracted state-space representations).
- Development and implementation of MPC approaches for robust coordination of physically coupled subsystems.
- Investigation and evaluation of resilience mechanisms under uncertainty, disturbances and fault conditions, including cascading effects.
- Building and using simulation environments to validate the developed methods.
- Preparing scientific publications and presenting research results at international conferences.
- Supporting student supervision and contributing to the general academic activities of the group.
Required qualifications:
- University degree (Diploma, Master) with grades in Electrical Engineering, Information Technology, Mechanical Engineering, Technical Cybernetics, Applied Mathematics, or a comparable field. The required degree must be completed by the start of employment.
- Very good knowledge of systems theory and automatic control, especially in optimization-based, hierarchical, and distributed control approaches.
- Strong interest in both theoretical development and practical implementation of modern control methods.
- Independent, structured, and team-oriented working style.
- High motivation and ability for interdisciplinary collaboration.
- Very good verbal and written communication skills in English.
Further desirable qualifications:
- Knowledge of model identification and model reduction.
- Experience with modelling and control of networked or physically coupled nonlinear systems.
- Knowledge in the field of robust control.
- Background in energy systems with a high share of renewable generation.
- Experience with development and simulation environments (e.g., MATLAB/Simulink, Julia, Python).
- Strong organizational skills.
- Good verbal and written communication skills in German, or willingness to acquire them.
For further information please contact Prof. Dr.-Ing. Christian A. Hans, Tel.:
In the event of any differences between the German and English versions of this call for application, the German wording is legally binding.
Our Offer:
As an employee of the University of Kassel
- you will be offered an interesting and diverse range of tasks within the framework of a modern and ambitious university,
- you will be part of an interdisciplinary team with a good and collegial working atmosphere,
- you will have the opportunity to participate in professional and interdisciplinary further education measures,
- is your workplace with good connections to public transport, which you can currently use free of charge.
In addition, you will benefit from the advantages of employment in the public service such as:
- an additional company pension (VBL),
- an optional child supplement in accordance with TV-Hessen, a family-friendly university (including childcare for emergencies),
- an annual bonus
- an entitlement to capital-accumulation benefits,
- a promotion of voluntary commitment,
- low-cost participation in university sports and a full range of fitness activities as part of Unifit, as well as workplace health management
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Please send your application with the usual informative documents, stating the reference number in the subject line, via the online form. We have compiled further information for you in our FAQ.
In exceptional cases, we will also accept your application documents in paper form addressed to: The President of the University of Kassel, 34109 Kassel, Germany, or via mail to bewerbungen[at]uni-, stating the reference number.
In the case of postal applications, please submit only copies of your documents (no folders), as these cannot be returned. All documents will be destroyed after completion of the selection process in compliance with data protection regulations.
The protection of your personal information is very important to us, so we will handle your personal information with care. By your application, you allow us the storage and use in the sense of the Hessian Data Protection and Freedom of Information Act. You can object to this at any time. Your personal data will be deleted.
Information according to Art. 13 DSGVO for the application process at the University of Kassel can be found at:
Information and FAQ about applying for a job offer:
The University of Kassel is very interested in the professional satisfaction of its employees. It is distinguished as a family-friendly university and, in the interests of equal opportunities, strives to offer everyone the same opportunities for development and to counteract existing disadvantages. It promotes the Family Welcome Service and also the Dual Career Service for positions that are scientifically and academically filled. One of the strategic goals of the University of Kassel is to significantly increase the proportion of women in research and teaching. Applications from women are therefore particularly welcome. Seriously handicapped and equivalent applicants are given preference if they have the same suitability, qualifications and professional performance. Full-time positions are generally divisible (except when filling civil servant positions). Applications indicating the Position Number, which may be in digital form, should be sent to the President of the University of Kassel, 34109 Kassel, Germany quoting the applicable reference number.
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