Postdoctoral Position in Generative Models of Protein-protein Interactions - Group Diss

Postdoctoral Position in Generative Models of Protein-protein Interactions - Group Diss

FMI - Friedrich Miescher Institute for Biomedical Research

Basel, Switzerland

The group of Dr. Guillaume Diss at the Friedrich Miescher Institute for Biomedical Research (FMI) aims to study the genetic architecture of protein function - specifically, how the sequence of a protein determines its function.

Protein-protein interactions are essential for most biological functions, and understanding how the sequence of interacting proteins determines the affinity and specificity of these interactions is crucial. This understanding will improve our ability to predict the functional impact of genetic variation and enable the design of new synthetic proteins for therapeutic or bioengineering applications.

Recent advances in artificial intelligence and generative biology have paved the way for such developments. However, training deep-learning models to predict affinity from sequence requires vast amounts of data that capture the wide and complex genetic landscape of protein-protein interaction affinity.

Our lab has developed and optimized a deep mutational scanning approach, deepPCA, which allows us to quantitatively measure the affinity of millions of interactions variants. We are seeking a highly motivated postdoctoral researcher to leverage this rich dataset and train the next generation of generative models capable of predicting protein-protein interaction affinity from sequence data.

In this role, you will:

• Lead independent research projects.
• Collaborate with other team members and work closely with the Zenke lab (our experts in AI) and the Computational Biology facility to analyze the data generated and develop thermodynamically-inspired deep-learning models.
• Contribute to scientific publications in peer-reviewed journals.

About you:

• You hold a PhD in Biology, Physics, Computational Sciences, or a related field.
• You have experience in deep learning and a solid background in biophysics and thermodynamics.
• You have a strong publication record.
• You can communicate clearly and effectively in English and enjoy working collaboratively in a team.

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