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Field
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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sciences. Molecular and Structural AI: Hands-on experience with novel AI approaches to molecular dynamics simulations, protein/antibody structure prediction and design, retrosynthesis, and multi-modal drug
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on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
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informative but also pose significant privacy risks. Your work will focus on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic
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advanced statistical methodologies, including several of the following: Survival analysis Hierarchical and mixed-effects models Clinical trial design and analysis Structural equation modeling Bayesian data
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated experience applying AI to the SIT’s
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workflow driven by a Bayesian optimization engine — translating algorithmic experimental directives into machine actions and returning structured results to the decision-making system. Responsibilities
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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Research Engineer/Postdoctoral Position Decision and Bayesian Computation (DBC) – Epiméthée (EPI) Laboratory Institut Pasteur, Paris | 25 rue du Docteur Roux, 75015 Paris Position Overview We
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, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the