Our offer:

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. Focal points in 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.

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

• Leading a highly motivated team of international researchers.
• Conferences, summer schools and various continuing education offers.
• Use of an own, very large Slurm-based computer cluster (CPU and GPU).
• From 2022 (planned) use of an own test vehicle (including camera, RADAR, LIDAR).
• Development of new methods that are used in practical applications.
• Support for further scientific qualification.

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 (if you work at Holländischer Platz or Wilhelmshöher Allee) 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.

Your tasks:

Independent research tasks within the AI-DT project, particularly in the following areas:

• Labeling methods for highly automated annotation of data (e.g., images and trajectories) for the training of machine learning methods (especially deep learning approaches).
• Active learning methods for object detection in camera images and for intent detection. (i.e., activity detection in time series) of unprotected traffic participants.

• Modeling methods for quantifying aleatory and epistemic uncertainty in Deep Learning.
• Methods for exploiting the information, gained from sensor fusion for the highly automated annotation of camera, LIDAR, and RADAR data.
• Responsible team leader role of a research group of the department IES in the field of AI and machine learning in the automotive sector with project responsibility and management for various third-party funded projects in the automotive sector.
• Independent acquisition of further third-party funded projects and collaboration on project proposals.

Requirements:

• PhD in Computer Science or other relevant field completed with very good results.
• Very good fundamental knowledge in machine learning and data analysis.
• Successful scientific career with publications in the relevant fields of activity.
• Experience in obtaining third-party funding (e.g. BMBF, BMWI, DFG).
• Experience in the application of machine learning methods, especially in the area of deep learning.
• Experience in programming especially in Python, but also other programming languages.
• 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.

Of advantage are:

• Experience in active learning for object detection in images and experience in time series analysis, especially intention detection and activity recognition.
• Strong knowledge of AI tools, e.g., PyTorch, Tensorflow, Sklearn, Pandas, OpenCV, and Numpy.
• Organizational and coordination experience (e.g., project management).

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

Further Information