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We are looking to host a Marie Skłodowska-Curie Postdoctoral Fellow and support applications to the MSCA PF 2025 call.
What we offer
Join an established international collaboration between young PIs at the intersection of AI and experimental biology with a proven track record of impactful research and extensive industry experience
Experience working at top research universities and institutes at the forefront of protein design, i.e. Technical University of Munich (DE), Duke (US), and Helmholtz (DE)
Collaborate with experimentalists to learn about interesting biology and validate your in-silico work through wet lab experiments
Work on high-impact problems in protein design and synthetic biology with insights from industry
Join a vibrant local ecosystem of labs and other for instance through locally organised meet-ups like the Protein Design Stammtisch (DE) and the Triangle Protein Design (TriPoD) seminar series (US), and offers access to top-tier compute and lab resources
What we’re looking for
We’re looking for a curious, self-driven PhD with a background in computational biology, bio/physics or computer science, and:
Strong experience in applied Deep learning (e.g., model training using PyTorch or TensorFlow)
Understanding of current trends in AI (transformer models, GNNs, SE(3)-equivariant networks, diffusion models, etc.)
Bonus: Hands-on work with protein language models (e.g. fine-tuning), structural prediction tools (AlphaFold, ESMFold), or distributed training
Interested?
Reach out to us with your CV and a short statement of interest (email: msca@learning.bio). We'll help develop a strong application for the MSCA Postdoctoral Fellowship (please reach out by July 15th; Application deadline: 10 September 2025).
About us
Michael got his PhD (Dr. rer. Nat.) in Bioinformatics (summa cum laude) from the Technical University of Munich in 2022. His thesis, "How to Speak Protein? - Representation Learning for Protein Prediction" focused on adapting representation learning methods from natural language processing to protein sequences. Importantly, he was among the first to demonstrate the practical usefulness of the learnt protein representations for a variety of structural and functional protein features. The relevance of his thesis was honored independently by being awarded among the finalists for the Deutsche Studienpreis, an award honoring the most influential dissertation within Germany every year. After finishing his PhD, Michael was among the first to expand the input repertoire of protein language models towards making protein 3D structures amenable to protein language models (pLMs), rendering them multi-modal.
While working at Sanofi, Michael worked as a Computational Scientist within the newly formed "Biologics x AI Moonshot" (BioAIM) team to apply and develop predictive and generative AI approaches for Biologics research which led to a successful patent application.
Since March 2025, Michael leads his own team within the Institute of Computational Biology (ICB) at Helmholtz Munich and works as a lecturer at the Technical University of Munich.
For more details on Michael's scientific work and other achievements, please refer to his Google Scholar profile and CV.
Chris earned his PhD in Informatics (summa cum laude) from the Technical University of Munich in 2023. During his doctoral studies, he made significant advances in bio-sequence representation learning, helping to establish the field—particularly through pioneering work on transformer models for proteins and nucleotides. His research also encompassed a wide range of applied bioinformatics challenges, resulting in impactful software and data solutions that have enabled new scientific discoveries. Notably, Chris played a key role in launching rigorous evaluation standards for protein models in protein design and engineering, introducing gold-standard benchmarking datasets and innovative data splitting analyses.
In his first two years at NVIDIA, Chris was instrumental in shaping the company’s digital biology strategy. He contributed to product development, ecosystem building, and leadership initiatives, helping NVIDIA become a leading community contributor in digital biology. His work has driven the release of influential open-source software, addressing challenges from accelerated homology search to biological design.
As of 2025, Chris holds a dual appointment: he is a Visiting Assistant Professor at Duke University and leads an applied research group in Digital Biology at NVIDIA. At Duke, he focuses on exploratory research in biological machine learning (BioML), while at NVIDIA, he leads applied research in accelerated drug discovery.
You can find Chris’s Google Scholar profile here, and more information about his background and achievements in his CV.
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