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Nancy

    Postdoctoral fellowship in molecular simulations, machine learning, and statistical mechanics - Nancy, France - Université de Lorraine

    Université de Lorraine
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    Description

    We are looking for a highly motivated and talented postdoctoral candidate with interests in molecular simulations, machine learning, and statistical mechanics for research opportunities in:Development of data-driven schemes for the discovery of slow degrees of freedomMolecular simulations of complex mechanisms in large biological objectsComputational modeling of transition pathways in high-dimensional systemsPrior experience in computer programming, machine learning algorithms, and molecular simulations of biological systems is highly desirable, but not necessary.

    A strong background in physical sciences, preferentially in physics, and especially in statistical mechanics, is required.

    Christophe ChipotTERMS AND TENUREThis two-year position will be based at the Laboratoire International Associé CNRS/UIUC, Laboratoire de Physique et Chimie Théoriques, UMR n°7019, Université de Lorraine, BP 70239, 54506 Vandœuvre-lès-Nancy cedex, France.

    The duration cannot exceed 24 months.
    The target start date for the position is May 31, 2024, with some flexibility on the exact start date.

    HOW TO APPLY Applicants are requested to submit the following materials:


    • A cover letter applying for the position
    • Full CV and list of publications
    • Academic transcripts (unofficial versions are fine)Deadline for application is May 15, 2024. Applicants will be interviewed by an Ad Hoc Commission by May 15, 2024.Applications are only accepted through email, and will only be considered if they meet the criteria of experience detailed above. All documents must be sent to LOCATIONNancy, Lorraine, France


  • Université de Lorraine Nancy, France

    Offer DescriptionWe are looking for a highly motivated and talented postdoctoral candidate with interests in molecular simulations, machine learning, and statistical mechanics for research opportunities in:Development of data-driven schemes for the discovery of slow degrees of fr ...

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