Postdoc Researcher - Machine Learning and Pattern Recognition (m/f/d)

Ihre Aufgaben
The central nervous system (CNS) is our most complex organ system. Despite tremendous progress in our understanding of the biochemical, electrical, and genetic regulation of CNS functioning and malfunctioning, many fundamental processes and diseases are still not fully understood. The CRC 1540 ‘Exploring Brain Mechanics’ synergizes the expertise of engineers, physicists, biologists, medical researchers, and clinicians in Erlangen and Berlin to exploit mechanics-based approaches to advance our understanding of CNS function and, as a long-term vision, to provide the foundation for future improvement of diagnosis and treatment of neurological disorders.
Within the consortium, a wealth of genetic, biochemical, mechanical, and imaging data is acquired in different projects, spanning different species, experimental settings, and modalities. To utilize this data to its full extent and support quantification of experimental results, your tasks will include the development of tools and deep learning approaches for different tasks as well as fundamental research in machine learning and computer vision. Specifically, we aim to develop translatable annotation tools and processing pipelines on the one hand, and on the other hand, we will explore fundamental research questions in machine learning related to domain generalization, transfer learning, and use of self-supervised learning. This project offers an opportunity to work in a creative, interdisciplinary, and inspiring work environment where you collaborate with renowned partners from research, medicine, and industry to design and implement innovative projects at the intersection of fundamental machine learning and applied research.

Notwendige Qualifikationen:

• A PhD or equivalent research experience in computer science, medical engineering, or a relevant adjacent field, on machine learning and image analysis, ideally with a focus on biological or medical data.
• Proficiency in Python, with strong coding and debugging skills.
• Experience with deep learning frameworks such as PyTorch, JAX, TensorFlow, or similar libraries. Experience with Django and Nginx is a plus.
• Proven research experience in this field, typically via peer-reviewed publications in high-quality journals and ML/CV/Med Imaging conferences.
• Experience working with (medical) image data, ideally microscopic and / or multimodal image analysis.
• Experience with the design and development of data sets and data annotations.
• Strong proficiency in English, both written and spoken.
• Ability and willingness to work both independently and collaboratively on different research questions.
• Experience working in a highly interdisciplinary team, strong communication skills.

Stellenzusatz
Befristetes Forschungsvorhaben

• The project is a collaboration between the Department AIBE and the Pattern Recognition Lab at FAU Erlangen-Nürnberg and the Professorship for Pattern Recognition at the University of Würzburg; the workplace can be arranged to be predominantly located either in Erlangen or in Würzburg.
• FAU and EBM support career development for postdocs and early career researchers on multiple levels, including startup funding for additional research projects, and mentoring.
• Possibility for part-time remote work (home office).
• The position is available immediately. The project is currently funded until Dec 2026, a further extension of the position is envisioned.

You can find more information about us and our research here:
Exploring Brain Mechanics:

• https://www.crc1540-ebm.research.fau.eu/

AIMI Group and Department Artificial Intelligence in Biomedical Engineering:

• https://www.aimi.tf.fau.de/
• https://www.aibe.tf.fau.de/

Professorship for Pattern Recognition, located at the Center for AI and Data Science (CAIDAS, Uni Würzburg):

• https://www.caidas.uni-wuerzburg.de/pr/
• https://www.caidas.uni-wuerzburg.de/

FAU promotes equal opportunities. We aim to foster a collaborative and inclusive research environment where interdisciplinary ideas thrive. We welcome researchers from diverse backgrounds and encourage applications from those traditionally underrepresented in AI and healthcare. Female candidates are specifically encouraged to apply.