Research Associate , EG 14 TV-H, at the Intelligent Embedded Systems Group in the Department of Electrical Engineering/Computer Science

The Intelligent Embedded Systems (IES) group conducts research in the area of fundamentals and applications of methods in 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, and techniques for real-time analysis of time series. Applied research focuses on the fields of energy systems, automotive (autonomous driving) and experimental physics/materials. The IES group currently employs around 20 researchers in the above areas.

Your tasks:

• Methods for perceiving cyclists’ behaviour by using machine learning techniques (in particular deep learning approaches) based on data from vehicle sensors (e.g. camera and LiDAR data).
• Cross-labelling approaches for the highly automated annotation of cyclists’ behaviour data, i.e. transferring ‘labels’ from, for example, camera data to LiDAR data.
• Methods for integrating information from models trained on different data modalities and evaluating the resulting information gain.
• Planning and conducting measurement campaigns in the form of laboratory experiments as well as field experiments.
• Research within the DyNaMo project.
• Contributing to the group’s general tasks., * you will have the opportunity to attend conferences, summer schools, and various professional development programmes (e.g. the opportunity to obtain a habilitation degree),
• you will have access to your own, very large Slurm-based compute cluster (CPU and GPU),
• you will have access to your own, state-of-the-art test vehicle (a car equipped with a camera, LiDAR and radar),
• you will be able to develop new methods for use in practical applications.

In case of any questions ask Prof. Dr. B. Sick, 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 .

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 , 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.

• A PhD and a completed university degree in Computer Science or a closely related field
• Excellent knowledge in perception and computer vision.
• Very good fundamental knowledge in machine learning and data analysis.
• A successful academic career with high-impact publications in the relevant fields.
• Experience in external fundraising (e.g. LOEWE, BMFTR, DFG).
• Experience in programming, particularly in Python, but also in other programming languages.
• Good knowledge of AI tools, e.g. PyTorch, TensorFlow, scikit-learn, Pandas, OpenCV and NumPy.
• A structured approach to work that enables you to lead a team.
• A keen interest in the challenges posed by machine learning applications in the field of automated driving.
• An independent and goal-oriented approach to work, and a passion for scientific research.
• Very good written and spoken German and English (at least B2 level in each).
• A driving licence valid in Germany for passenger cars.

The following conditions are advantageous

• Experience in organisation and coordination (e.g. project management, team leadership).
• Experience in supervising PhD students, e.g. when they write publications.

The University of Kassel is a modern and growing University with round about 21.000 students. The university’s wide range of expertise covers subjects in the natural, applied, cultural and social sciences., 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.