The University of Kassel is a modern and growing University with round about 25.000 students. It has an extraordinary wide section with expertise of nature, technique, culture and society.
The following position in the Department of Electrical Engineering / Informatics at the University of Kassel is open for applications:
Scientific Assistant (m/w/d), EG 13 TV-H, fixed-term and full-time position (currently 40 hours per week)
|Date of Hire:
||as soon as possible
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 full-time position is initially limited until 31.03.2023 within the project “KI-Data Tooling (KI-DT), funded by the BMWI according to the project duration (fixed-term(§ 2 Abs. 2 WissZeitVG); opportunity for PhD project). An extension of the employment is planned.
The Department “Intelligent Embedded Systems” (IES) conducts research in the area of foundations and applications of methods of data analysis, machine learning (e.g., deep learning, active 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 employees working in the above-mentioned areas.
Research within the KI-DT project, particularly in the following areas:
- Methods for highly automated annotation of data (e.g., camera, LiDAR, and RADAR data for the training of machine learning methods (especially deep learning approaches).
- Active learning methods for object detection and for intention detection of vulnerable road users.
- Modeling methods for quantifying aleatoric and epistemic uncertainty in Deep Learning.
- Methods for fusing information from several sensors such as LiDAR, RADAR, or cameras for the highly automated annotation of data.
- Master in Computer Science completed with very good result, preferable in the above areas.
- Very good fundamental knowledge in machine learning and data analysis, preferable in the above areas.
- Experience in the application of machine learning methods, especially in the area of deep learning, preferable 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 and lead in a team.
- Curiosity for challenges in machine learning applications in the field of automated driving.
- Independent and goal-oriented work style and enjoyment of scientific work.
- Good knowledge of German and English, both written and spoken (at least comparable to B2)
As an employee of the department of Intelligent Embedded Systems you will
- be part of a highly motivated team of international researchers,
- be able to participate in conferences, summer schools and various offers for career advancement (e.g. to qualify for a PhD),
- use an own, very large Slurm-based computer cluster (CPU and GPU),
- be able to use an own test vehicle (including camera and LiDAR), which is planned for 2023,
- be able to develop new methods that are used in practical applications.
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,
- your workplace is centrally located in the city of Kassel with good public transport connections, which you can currently use for free.
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.
For further information please contact Prof. Dr. Bernhard Sick, tel.: +49 561 804-6020, E-mail: bsick(at)uni-kassel.de.
You can find more jobs at stellen.uni-kassel.de
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:
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 or email@example.com, quoting the applicable reference number.