The post is initially limited until 31 December 2028 as part of the ‘DyNaMo’ project, in accordance with its duration (academic qualification position pursuant to § 2 WissZeitVG). An extension of the employment is planned. The opportunity to obtain a habilitation degree is given.
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.
*Requirements:**
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.
As a member of staff in the Intelligent Embedded Systems group:
You will lead a small but highly motivated team,
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
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