The Scientific Computing Center (SCC) is the Information Technology Center of KIT. The junior research group "Robust and Efficient AI” at SCC develops scalable, energy-efficient machine learning methods for data-intensive applications in the natural sciences, such as weather forecasting and medical diagnostics. Our research combines modern AI with high-performance computing (HPC) to enable robust analysis of extremely large datasets.

We are seeking a Postdoctoral Researcher to join the BMFTR-funded project MorphoSphere, which focuses on AI-based analysis of ultra-high-resolution 3D imaging data generated at large-scale neutron and synchrotron facilities (e.g. DESY, ESRF). These datasets enable advances in materials science and biomedical research, but they easily reach petabyte scale and cannot be processed with standard deep learning approaches. Within this project, you will develop and implement machine learning approaches that enable processing these large 3D image at full resolution.

Your contributions will include:

• Developing parallel and distributed algorithms for processing ultra-large 3D images across multiple GPUs
• Designing and adapting scalable deep learning models for segmentation and landmark detection (e.g. U-Net variants, Transformers)
• Extending and optimizing existing approaches within the HeAT software framework
• Implementing solutions using GPU-based HPC systems and modern supercomputing architectures
• Applying and validating methods on real-world synchrotron and neutron imaging data
• Contributing to research software engineering (RSE) best practices, including maintainable, reproducible, and high-quality research code
• Publishing results in peer-reviewed venues and collaborating with domain scientists

Persönliche Qualifikation

Job requirements:

• You hold a Degree (Masterlevel) and PhD in computer science, physics, mathematics or equivalent discipline
• Very good programming and software development skills, preferably in Python
• Proven academic track record (publications) in deep learning model development, high-performance computing or parallel algorithms research
• Prior experience in data science applications for the natural sciences and engineering

Curious about an exciting and versatile role in an agile team? Discover more about SCC as your professional place to be: https://www.scc.kit.edu/en/aboutus/working-at-scc.php

Organisationseinheit

Salary

Salary category 13 TV-L, depending on the fulfillment of professional and personal requirements.