PhD Position - AI Models for Membrane Protein Design

About the role

We are looking for a motivated PhD student to develop next‑generation AI‑driven methods for membrane protein and nanopore design. The project combines large‑scale machine‑learning on structural and sequence data with membrane‑specific biophysical constraints to enable engineering of synthetic membrane proteins for transport, sensing and signal transduction.

Key responsibilities

• Design and implement machine‑learning models that incorporate protein‑lipid and protein‑membrane interactions.
• Train models on extensive structural and sequence datasets of membrane proteins.
• Integrate biophysical constraints specific to membranes into the design pipeline.
• Collaborate with experimental groups for validation using cryo‑EM, X‑ray crystallography, NMR and high‑performance computing resources.
• Contribute to publications and present results within the RosettaCommons community.

Required profile

• Master’s degree (or equivalent) in Computational Biology, Bioinformatics, Computer Science, AI/ML, Physics, Mathematics, Bioengineering, Structural Biology, Biophysics or a related field.
• Strong quantitative, analytical and problem‑solving abilities.
• Excellent written and spoken English communication skills.
• Ability to work both independently and collaboratively in an international research environment.

Required skills

• Programming in Python or a comparable scientific language.
• Experience or strong interest in machine learning and deep learning.
• Knowledge of structural bioinformatics and data‑driven modelling.

What we offer

• Access to state‑of‑the‑art facilities including cryo‑EM, X‑ray, NMR and high‑performance computing.
• Interdisciplinary research environment with close ties to experimental validation.
• Opportunities to collaborate with leading experts in protein engineering, structural biology and nanopore technology.