Postdoc: Machine Learning for Peptide Membrane Permeability Prediction

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Postdoctoral Position in Machine Learning for Peptide Membrane Permeability Prediction . Location: GEC Laboratory, CNRS/Picardie University, Amiens, France. Duration: 12 months (start date flexible, before end of 2026). Salary range: €2700 to €3000 net monthly (including medicare). Application Deadline: Open until filled. Project Context . Join a cutting-edge project at the intersection of computational chemistry, biophysics, and machine learning to address one of the most pressing global health challenges: antimicrobial resistance . The project, funded by the MAIA initiative ( https://maia.a2u.fr ) , aims to develop predictive models for the membrane permeability of antimicrobial peptides (AMPs) and cell-penetrating peptides (CPPs) —key candidates for next-generation antibiotics and drug delivery systems. Despite their therapeutic potential, AMPs/CPPs rarely reach clinical trials due to challenges in evaluating their membrane affinity and permeability , which are critical for efficacy and toxicity. This project will leverage COSMOperm (a thermodynamic model for membrane permeability) and graph neural networks (GNNs) to create a high-throughput screening tool. The successful candidate will use and contribute to ADAPTABLE , a web platform hosting >40,000 AMPs, and collaborate with experts in quantum chemistry (LG2A) and AI-driven molecular modeling (GEC). Key Responsibilities . Data