Health and Life Science Alliance - Interinstitutional Postdoc Program 2026

We are seeking two highly motivated postdoctoral researchers to join our team through the Health and Life Science Alliance Postdoc Program 2026!

In the current call we are proposing two exciting directions in collaboration with the Furlong lab (EMBL) and the Dieterich lab (University Hospital HD).

• Postdoc Position in collaboration with the Furlong Lab, EMBL: Decoding gene regulation through integrated sequence-to-function modeling of genetic variation
• Postdoc Position in collaboration with the Dieterich Lab, University Hospital HD: Interpretable Generative Deep Learning Models for improved Nanopore RNA signal processing to robustly detect RNA modifications and structure

For more information please visit the Health and Life Science Alliance homepage

The Alliance offers funding for two-year postdoctoral positions.

• Postdoctoral applicants have to apply with their own research proposal, based on the project outline
• Applicants must contact both supervisors prior to applying to discuss whether there is a good match. This has changed compared to the last calls.
• Applications require a letter of support from both PIs
• Applicants must be early career postdoctoral researchers, and have at a minimum one first author paper (preprints and papers submitted but not accepted do not count).
• Must have less than four months of postdoc or clinician scientist experience at Alliance institutions (co)hosting the new project at the time of the submission deadline.
• Must complete a PhD, MD, or MD/PhD degree latest within four months after the submission deadline. Expected start date: between August 1, 2026 and latest by January 1, 2027.

Application deadline: March 31st, 2026

We are looking for a highly motivated PhD candidate with a Master's degree in Computer Science, Computational Biology, Bioinformatics, or a related field to join our team! A strong background in computational biology and machine learning, particularly with PyTorch or similar deep learning frameworks, is essential.

If you have experience in deep learning for genomics, this is an exciting opportunity to investigate the cis-regulatory syntax of the transcriptome at both post-transcriptional and translational levels. Your work will contribute to the interpretation of disease-associated non-coding variants, help uncover their mechanisms of action, and elucidate their relevance for human health. It will also advance our understanding of the principles governing post-transcriptional and translational gene regulation, with the long-term goal of applying these insights to the design of synthetic regulatory elements.

If this sounds like you, we'd love to hear from you!

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