Job
In dieser Position entwickelst du Methoden zur Wahrnehmung des Verhaltens von Radfahrern mithilfe von Deep Learning, unterstützt Messkampagnen und leitest ein engagiertes Team bei innovativen Forschungsprojekten.
Job-Technologien
Deine Rolle im Team
- 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.
Unsere Erwartungen an dich
Ausbildung
- A PhD and a completed university degree in Computer Science or a closely related field.
Qualifikationen
- 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.
- 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.
Erfahrung
- Experience in external fundraising (e.g. LOEWE, BMFTR, DFG).
- Experience in programming, particularly in Python, but also in other programming languages.
Unser Angebot
- 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.
- 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.
- 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 has good connections to public transport, which you can currently use free of charge.
- 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.
Benefits
Gesundheit, Fitness & Fun
Work-Life-Integration
Themen, mit denen du dich im Job beschäftigst
Job-Standorte
Das ist dein Arbeitgeber
Die Universität Kassel ist eine dynamische Universität mit rund 23.000 Studierenden. Sie hat ein außergewöhnlich breites Profil mit den Kompetenzfeldern Natur, Technik, Kultur und Gesellschaft.
- Etablierte Firma
- Hybrid, Onsite
- Bildungswesen
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