Doctorant F / H Deciphering long-range communications within macromolecular complexes

INRIA; Villers-lès-Nancy; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Un institut de recherche en informatique recherche un doctorant pour le projet DynaNova, centré sur la dynamique conformationnelle et la communication allostérique dans les complexes macromoléculaires. Le candidat développera des architectures de réseaux de neurones graphiques novatrices et travaillera au sein d’une équipe interdisciplinaire. Ce poste offre également des avantages sociaux et une rémunération de 2300 € brut par mois, avec possibilité de télétravail après 6 mois.

Prestations

Restauration subventionnée

Transports publics remboursés partiellement

7 semaines de congés annuels

Possibilité de télétravail

Accès à la formation professionnelle

Qualifications

• Master en Informatique, Machine Learning, ou domaine connexe requis.
• Expérience forte en deep learning, idéalement avec PyTorch.
• Connaissances pratiques de Python et bonnes pratiques de codage.

Responsabilités

• Concevoir et mettre en œuvre des architectures de réseaux de neurones graphiques.
• Intégrer des caractéristiques dynamiques et géométriques issues de simulations MD.
• Collaborer avec des étudiants de master et doctorat.

Connaissances

Deep learning

PyTorch

Graph neural networks

Python

Bioinformatics

Formation

Master en Informatique, Machine Learning, Bioinformatique ou Biologie Computationnelle

Description du poste

Contexte et atouts du poste

This 3-yearPhD position is funded by the prestigiousProgramme Inria Quadrant (PIQ)for the project DynaNova , which aims to advance our understanding of conformational dynamics and allosteric communication in macromolecular complexes. The successful candidate will develop novel co-operative message-passing graph transformer architecture that learns conformational heterogeneityfrom molecular dynamics (MD) simulations by encoding the underlying dynamics of atomic interactions and correlations inmacromolecular complexes. You will join the Delta team at Inria (Université de Lorraine), working closely with Dr. Yasaman Karami and Dr. Hamed Khakzad , experts in conformational dynamics, allostery, and deep learning for structural biology. The team is growing and offers a highly interdisciplinary environment that brings together researchers in structural bioinformatics, computational chemistry, biophysics, and machine learning. We have access to major national HPC facilities (Grid5000, Jean Zay, GENCI allocations), including large-scale GPU resources.

Mission confiée

Biomolecular function is driven by both structure and dynamics . Understanding long-range communication within macromolecular complexes is essential for deciphering molecular mechanisms and for developing therapeutic strategies. While deep learning has revolutionized structural prediction (e.g., AlphaFold2),allosteric signaling remains poorly understood, largely due to the scarcity of dynamic data. Our group recently developed :

• DynaRepo, a database of molecular dynamics trajectories of more than 700 macromolecular complexes (~5.5 bilions of frames) [1]. DynaRepo is the first MDDB node in France : - ComPASS, a graph-based method for identifying communication networks in protein–protein and protein–nucleic-acid assemblies [2].
• DynamicGT, a dynamic-aware graph transformer for predicting binding sites in flexible and disordered regions [3].

Building on these foundations, DynaNova will leverage a large MD dataset (DynaRepo) and advanced GNN / Transformer models to uncover long-range communication pathways within macromolecular complexes. ThePhD candidatewill lead the development of an innovative deep learning framework to learn conformational heterogeneity and decode long-range communications within macromolecular complexes.

References :

[1] Mokhtari, O., Bignon, E., Khakzad, H., & Karami, Y. . DynaRepo : the repository of macromolecular conformational dynamics.Nucleic Acids Research, gkaf1130.

[2]Bheemireddy, S., González-Alemán, R., Bignon, E., & Karami, Y. . Communication pathway analysis within protein-nucleic acid complexes.Journal of Chemical Theory and Computation.

[3]Mokhtari, O., Grudinin, S., Karami, Y., & Khakzad, H. . DynamicGT : a dynamic-aware geometric transformer model to predict protein binding interfaces in flexible and disordered regions.bioRxiv.

Principales activités

• Design and implement novel graph neural network architectures (Message-passing GNNs, Graph Transformers, graph community-aware latent spaces).
• Integrate dynamic, geometric, and biophysical features extracted from large MD simulations.
• Train and benchmark models on state-of-the-art datasets.
• Collaborate with PhD and Master students , contributing to supervision and scientific guidance.
• Contribute to publications, open-source software development, and conference presentations .
• Participate in the preparation of a webserver and tools to disseminate the developed methods.

Compétences

• Masterin Computer Science, Machine Learning, Bioinformatics, Computational Biology, or related fields.
• Strong experience in deep learning , ideally with PyTorch .
• Proven experience with graph neural networks, geometric deep learning, or transformers is a major advantage.
• Practical knowledge of Python , clean coding practices, and reproducible ML workflows.
• Familiarity with protein structure, biophysics, or molecular modeling (MD, docking, etc.) is highly desirable.
• Ability to work independently, collaborate in a multidisciplinary environment, and communicate effectively in English.
• Applications without a strong machine learning and / or computer science component cannot be considered.

Avantages

• Restauration subventionnée
• Transports publics remboursés partiellement
• Congés : 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d\'autorisations d\'absence exceptionnelle (ex : enfants malades, déménagement)
• Possibilité de télétravail (après 6 mois d'ancienneté) et aménagement du temps de travail
• Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.)
• Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria)
• Accès à la formation professionnelle
• Sécurité sociale

Rémunération

2300 € brut / mois