法国皮卡第儒勒·凡尔纳大学招聘博士后—机器学习用于肽膜渗透性预测

法国皮卡第儒勒·凡尔纳大学招聘博士后—机器学习用于肽膜渗透性预测

Postdoc: Machine Learning for Peptide Membrane Permeability Prediction

Work Environment

GEC Laboratory (CNRS/UPJV): A dynamic team with expertise in antimicrobial peptides and AI applied to molecular modeling.

LG2A Laboratory (UPJV): Collaborative access to COSMOperm and quantum chemistry resources.

Resources: High-performance computing (GPUs), interdisciplinary network (MAIA Chemistry/Health axis), and potential for publication in high-impact journals.

Application Process

Interested candidates should provide the following:

CV (including publication list).

Cover letter (1–2 pages) detailing:

Your motivation for the project.

Relevant experience in machine learning or molecular modeling.

Preferred start date.

Names/contact details of 2–3 references.

Review of applications will begin immediately and continue until the position is filled.

Why Apply?

Impact: Contribute to global health by accelerating the discovery of antimicrobial therapies.

Innovation: Develop novel AI tools for drug design, with potential for patents/publications.

Network: Collaborate with MAIA’s interdisciplinary community (chemistry, health, AI).

Related Papers:

Bouvier, J. Chem. Theory Comput 2026, 22, 1215.

Ramos-Martin et al, Life Sci. Alliance 2019, 2, e201900512.

Leal et al, BBA Biomembranes 2026, 1868, 184525.

University of Picardie Jules Verne

Date Posted: Posted on 1 June 2026

Location: France

Salary: $38,000 - $42,000

Job Tags:

ai and computational biology, deep learning, Insilico drug design, molecular biophysics, thermodynamics

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 Generation: Use COSMOperm to compute thermodynamic properties for small oligopeptides

Model Development: Encode peptides as graphs, implement graph convolutional networks

Model Training/Validation: Optimize hyperparameters and train models to predict the permeability of linear and cyclic AMPs/CPPs.

Required Qualifications

PhD in Computational Chemistry, Bioinformatics, Machine Learning, or a related field.

Strong programming skills in Python (PyTorch, PyG, or similar libraries).

Experience with machine learning (deep learning, graph-based methods, or molecular modeling is a plus).

Familiarity with Linux/GPU computing and data analysis tools.

Proactive and collaborative mindset—ability to bridge chemistry and AI disciplines.

Desirable:

Knowledge of thermodynamic models (COSMO-RS/COSMOperm) or peptide biophysics.

Experience with molecular modeling and quantum chemistry.

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