Research Scientist 3D Geometric Medical Deep Learning

Research Scientist 3D Geometric Medical Deep Learning

The Institute of Medical Informatics at the University of Lübeck (Director: Univ.-Prof. Dr. H. Handels, Research Group: Prof. Dr. M. Heinrich) is looking to hire a Research Scientist (m/f/d) in research on privacy-preserving 3D medical deep learning full-time (38.7 hours per week). The position is initially limited to 28.02.2026 and begins on the 15th of April 2025. An extension is possible. The possibility of scientific qualification (beginning/continuation of a dissertation or habilitation) is given and is strongly supported. The project is financed by the AnoMed BMBF project and LABEL DFG project (project partner: Fraunhofer MEVIS, Philips Research). The position is part of the research group of Prof. Mattias Heinrich, who is often a pioneer for new methodological developments in the field of medical image analysis and deep learning and has won several Best Paper Awards at MICCAI, MIDL, and BVM and has published numerous works in high-ranking journals (TMI, MedIA).

The LABEL and AnoMed projects offer a unique opportunity to develop and validate novel algorithms on a comprehensive population study NAKO, which contains more than 30,000 full-body MRI scans, taking into account data security and privacy concepts.

The focus of the research is on the development of innovative algorithms for the precise localisation of the patient\’s internal organs based on the information of the external RGB(D) camera and an anatomical 3D atlas. In addition, it offers the opportunity to contribute to other research aspects, including domain adaptation, learning with differential privacy, self-supervised learning, and multimodal learning. The position does not entail any teaching obligations.

Tasks:

• Development, implementation, and experimental analysis of scientific methods that address the main objectives of the funded research project.
• Preparation of publications in international, peer-reviewed journals and presentations at (inter-)national conferences.

Requirements:

• A completed master\’s degree in the fields of computer science, mathematics, engineering, or related course of study.
• Programming knowledge in PyTorch or the willingness to acquire it quickly.
• Motivation to promote the fundamental further development of methods of artificial intelligence in medicine in a theoretically sound manner.
• Experience in medical image analysis, computer vision, or machine learning may be evidenced by publications in TMI, MedIA, MICCAI, MIDL, CVPR, etc.
• Ability and proactive approach to independent work and learning, good knowledge of English.

What we offer:

• Excellent and state-of-the-art interdisciplinary research environment and support for your scientific career (e.g., Dissertation or Habilitation).
• National and international collaborations within the research projects.
• Flexible working hours, mobile working, compatibility of family and work.
• NAH-SH/Deutschland public transport ticket, access to university sports facilities, discounts at the canteen on campus, and many other perks.
• Pay in accordance with the current tariff if the tariff requirements are met, up to pay grade E13 TV-L. Subject to a final job evaluation.

The University of Lübeck is a modern and cosmopolitan employer. We welcome your application regardless of your age, gender, cultural and social background, religion, worldview, disability, or sexual identity. We promote gender equality. In case of equal suitability, qualifications, and professional performance, female applicants will be given preference. If you are an applicant with a severe disability, your application will be given preference in case of equal suitability.

Should you have any further questions regarding the vacancy, please contact Prof. Mattias Heinrich ( , Phone +49 451 3101 5602). Please send your written application including the usual documents (cover letter with research interests 400-800 words, curriculum vitae, certificates, list of publications, and contact of a reference – e.g., their advisor) stating the reference number 1013/25 as a single PDF file by 28.03.2025 (date of receipt) at the latest to and

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