Doctoral Research Associate (Wissenschaftliche*r Mitarbeiter*in, salary level E 13 TV-L)

Overview

42,500 students and 7,750 employees in teaching, research and administration, all working together to shape perspectives for the future – that is the University of Münster. Embedded in the vibrant atmosphere of Münster with its high standard of living, the University’s diverse research profile and attractive study programmes draw students and researchers throughout Germany and from around the world.

The Computer Vision and Machine Learning Research Group at the Institute for Geoinformatics at the University of Münster is seeking to fill the following position of a for the externally funded project Collaborative Research Centre 1748 ‘Principles of Reproduction’ at the earliest possible date. We are offering a fixed-term full-time position (100 %) for 3 years.

The position is located in the research group of Prof. Benjamin Risse (Computer Vision and Machine Learning Systems) and embedded in the DGF-funded Collaborative Research Centre 1748 ‘Principles of Reproduction’. The CRC 1748 involves scientists of the University, University Hospital, and Max Planck Institute Münster as well as of the RWTH Aachen. Our central objective is to elucidate the genetic, molecular, and cellular mechanisms governing the formation and function of the testis, production and function of sperm, fertilisation, as well as early embryonic development – in both health and disease. To this end, we combine interdisciplinary research in artificial intelligence, molecular, structural, and cell biology as well as in physiology, biophysics, epi /genetics, (bio)informatics, and multimodal data analysis.

Project Description: The project focuses on developing and applying novel machine learning-driven computer vision approaches to analyze complex biological image data. Specifically, you will develop tailored algorithms to quantify testicular histological structures and characterize sperm and cilia motility patterns

• Developing tailored computer vision and machine learning algorithms for biomedical applications
• Implementing deep learning-based object detection and semantic segmentation for large-scale histological slide scans
• Analysing sperm and cilia motility using advanced tracking, optical flow, and behavioral analysis algorithms
• Characterisation of testicular tissue composition and cell types using foundation models and interactive annotation tools like MARTHA
• Collaborating with an interdisciplinary team of clinicians and basic scientists to translate algorithmic insights into clinical care
• Participating in the Integrated Research Training Group ‘Reproduction.MS PhD-Training Centre in Translational Science’
• Master’s degree in Computer Science, Geoinformatics, Physics, Mathematics, or a related quantitative field
• Strong interest in Machine Learning, Deep Learning, and their application to complex biological or medical imagery
• Previous experience with Python, PyTorch/TensorFlow, and computer vision libraries; experience with medical image analysis is a plus
• High motivation for scientific work and willingness to contribute to an interdisciplinary team
• Excellent English communication skills (spoken and written); German skills are advantageous

Advantages for you

• The opportunity to work at the cutting edge of AI in medicine within a highly collaborative environment
• Competitive, interdisciplinary, and international research environment with a track record of intense mutual collaboration
• Structured PhD-training programme with a wide range of and professional development opportunities
• Salary according to tariff agreement, extra annual payment, and company pension plan (VBL)
• A respectful and appreciative work environment within a diverse team

The University of Münster strongly supports equal opportunity and diversity. We welcome all applicants regardless of sex, nationality, ethnic or social background, religion or worldview, disability, age, sexual orientation or gender identity. We are committed to creating family-friendly working conditions. Part-time options are generally available.

We actively encourage applications by women. Women with equivalent qualifications and academic achievements will be preferentially considered unless these are outweighed by reasons which necessitate the selection of another candidate.

Are you interested? Then we look forward to receiving your application by .

Please send us your application electronically in PDF format including:

• Letter of motivation (max 2 pages)
• Summary of your Master‘s thesis (max 1 page)
• Transcript and scanned copies of your degree certificates
• Two letters of recommendation and/or names and contact details of two references

Please note that we cannot consider other file formats.