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The University of Kassel is a modern and growing University with round about 22.000 students. The university’s wide range of expertise covers subjects in the natural, applied, cultural and social sciences.

In the Faculty of Computer Science/Electrical Engineering, - Department Intelligent Embedded Systems (Prof. Dr. Bernhard Sick), the following position is to be filled as soon as possible:

Scientific Assistant (m/w/d), EG 13 TV-H, Temporary, full-time or part-time (currently 20 or 40 per week)

Or

Scientific Assistant (m/w/d), (EG 14 TV-H), Temporary, full-time or part-time (currently 20 or 40 per week)

Application deadline:

19.03.2025

Date of hire:

as soon as possible

vacancy number: 38147

Scientific Assistant (m/w/d), EG 13 TV-H, Temporary, full-time or part-time (currently 20 or 40 per week) Part-time with half of regular working hours of a full-time employee or full-time. The position is initially limited until 31.07.2027 within the project "KonSEnz – Kontinuierlich selbstlernende Vorhersagemethoden und Services in smarten Energiemärkten und -netzen: KI-Algorithmen und Modelle“, funded by the BMWK according to the project duration (according to WissZeitVG). The possibility to work towards a doctorate is given.

Or

Scientific Assistant (m/w/d), (EG 14 TV-H), Temporary, full-time or part-time (currently 20 or 40 per week) Part-time with half of regular working hours of a full-time employee or full-time. The position is initially limited until 31.07.2027 within the project "KonSEnz – Kontinuierlich selbstlernende Vorhersagemethoden und Services in smarten Energiemärkten und -netzen: KI-Algorithmen und Modelle“, funded by the BMWK according to the project duration (according to WissZeitVG).The possibility to work towards a habilitation is given.


Are you interested in developing cutting edge machine learning methods and in their application in our future energy system?

The department “Intelligent Embedded Systems” (IES) conducts research in the area of foundations and applications of methods of data analysis, machine learning, and artificial intelligence. In the focus of basic research are, for example, self-learning and self-organizing systems, methods of collaborative and active learning, methods of transfer learning, or techniques for real-time analysis of time series. In the area of applied research, the focus is on energy systems, automotive (autonomous driving) and experimental physics/materials. The IES department currently has about 25 researchers working in the above-mentioned areas. IES is member of the hessian.AI (The Hessian Center for Artificial Intelligence).

The aim of the KonSEnz project is to combine methods of continuous, adaptive learning and machine learning in operation in order to meet the growing requirements of a highly data-driven energy system in terms of automation, scaling and resilience in operation. For the intelligent use of flexibility through new consumers, generators and storage systems, the demands on operational planning and thus on the predictability of more and more components and players in the energy system are increasing. At the same time, the highly dynamic nature of the system requires constant adaptation and expansion of planning and forecasting models in real time. The associated processes such as model training runs and evaluations must therefore inevitably be taken over reliably and securely by highly autonomous, computer-aided processes.

The sub-project of the University of Kassel focuses on the development of algorithms and models with the help of machine learning and artificial intelligence methods. The focus is on methods of transfer learning and representation learning to facilitate the reuse of learned knowledge, methods of self-monitoring and self-awareness in technical systems to determine the need for model adaptation (e.g. for forecasting models) by the learning system itself, as well as methods of continuous learning with regard to self-reflection so that the learning system can adapt to new conditions (e.g. new plants) and guarantee the required performance. The results of the project are implementations of corresponding algorithms and models, which are being investigated in collaboration with project partners using use cases for wind power forecasts, photovoltaic forecasts and forecasts of vertical power flows at transformers.


Your tasks:

  • Research in the above-mentioned areas
  • Support of general tasks of the IES department
  • Writing publications
  • In case of TV-H EG 14: Independent work on fundamental and challenging scientific problems in the field of machine learning. Responsible for acquisition of third-party funded projects in the field of AI for energy. Participation in the supervision of doctoral students


Requirements:

  • Master in Computer Science (or closely related field) completed with very good result, preferably in the above areas. The degree required for this position must have been obtained by the starting date at the latest
  • In case of TV-H EG 14: PhD with a thesis on a related topic and a very good result. Very strong knowledge and experience in the above fields. Strong publication record and first experience in acquiring and leading projects. The degree required for this position must have been obtained by the starting date at the latest.
  • Very good fundamental knowledge in machine learning and data analysis, preferably in the above areas
  • Experience in the application of machine learning methods, especially in the area of deep learning, preferably in the above areas
  • Experience in programming especially in Python, but also other programming languages
  • Strong knowledge of AI tools, e.g., PyTorch, Tensorflow, Sklearn, Pandas, OpenCV, and Numpy
  • A structured working method that allows you to work in and lead a team
  • Independent and goal-oriented work style and enjoyment of scientific work
  • Very good knowledge of German and English, both written and spoken


Our offer:

As an employee of the department of Intelligent Embedded Systems you will

  • Being part of a highly motivated team of international researchers.
  • Participate in conferences, summer schools and various offers for career advancement (e.g. to qualify for a PhD or an academic career)
  • Use an own, very large Slurm-based computer cluster (CPU and GPU).
  • Develop new methods that are used in practical applications.


With your application, we require a convincing motivation letter and a clear statement whether you apply for a PhD (TV-H E13) or a PostDoc (TV-H E14) position (and to which fraction, e.g., 50% or 100%).


For any questions, please contact Prof. Dr. Bernhard Sick, tel.: +49 561 804-6020, e-mail: bsick(at)uni-kassel.de.


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

You can find more jobs at stellen.uni-kassel.de

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-kassel.de, 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:
www.uni-kassel.de/go/ausschreibung-datenschutz

Information and FAQ about applying for a job offer:

https://www.uni-kassel.de/uni/universitaet/stellenangebote/hinweise-und-faq-zur-bewerbung-auf-ein-stellenangebot

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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