Scientific Assistant (m/w/d), EG 13 TV-H or EG 14 TV-H, Institute of Inform

at  Universitt Kassel

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 Faculty Informatics/Electrical Engineering, Institute of Informatics/Electrical Engineering - Department IES (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)

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

• 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