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Urgent! Post-Doctoral Research Visit F/M Geometric deep learning models to study intrinsically disordered proteins Job Opening In Villers-lès-Nancy – Now Hiring INRIA

Post Doctoral Research Visit F/M Geometric deep learning models to study intrinsically disordered proteins



Job description

Contexte et atouts du poste

This postdoctoral position is part of the IDPFold project (2025-2029) recently funded by the French National Research Agency (ANR).

The main goal is to develop geometric deep learning models to study intrinsically disordered proteins (IDP).

The postdoctoral researcher will be supervised by Hamed Khakzad (Junior Professor, Inria).

Our team consists of two permanent researchers with several PhD and postdoc members, and is expected to grow by hiring new members.

Our main goal is to develop deep learning models, to study, and predict protein structure, interactions, function and to further design synthetic molecules.

The team has access to computational resources, including efficient GPUs and CPUs, from different cluster centers including Grid5000, Jean Zay, etc.

Mission confiée

IDPs are a large subset of proteins with no stable 3D structure on isolation.

They are involved in various cellular processes, and protein-protein interactions (PPIs).

One of the key aspects of IDPs is their ability to undergo disorder-to-order transition upon binding to a target structure.

While understanding this mechanism is essential, it remains an open problem in the field.

Novel approaches based on deep learning have started to make remarkable advances in protein structure and complex prediction.

However, the performance of these methods on PPI prediction where IDPs are involved is still lagging behind, mostly due to the complexity imposed by flexible regions.

This postdoc position aims to develop geometric deep learning models to elucidate this complex mechanism and will be potentially built on on-going research efforts in the team.

The postdoctoral researcher will have the possibility to co-supervise PhD students in the lab, involved in international collaborations, and will work closely with permanent researchers of the lab on this topic.

Principales activités

  • Implementing deep learning models

  • Contributing into training data collection and curation

  • Validating the method and analysing the results over SOTA benchmarks

  • Supervising Master and PhD students, collaborating with other teams

  • Writing scientific articles, software development and participating in international conferences
  • Compétences

  • PhD degree in Computer Science, or Bioinformatics

  • Proficiency in Python and good coding practices is mandatory

  • Experience in deep learning (PyTorch) is mandatory*

  • Knowledge in protein biochemistry

  • Ability to work independently and also to work in a team

  • Excellent oral and written English skills
  • *applications with no computer science/deep learning background will not be considered.

    Avantages

  • Subsidized meals

  • Partial reimbursement of public transport costs

  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)

  • Possibility of teleworking (after 6 months of employment) and flexible organization of working hours

  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)

  • Social, cultural and sports events and activities

  • Access to vocational training

  • Social security coverage
  • Rémunération

    2788 € gross/month


    Required Skill Profession

    Life Scientists



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