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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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model introduced previously for carburizing will be further developed in this study. In this model, carbon diffusion is predicted using Fick's law and finite difference scheme. A source term accounts for
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use numerical models to interpret and predict mechanical behavior. - Carry out a cross-analysis of experimental, microstructural, and numerical results. - Disseminate scientific results (publications
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predictive models of immune responses. Develop advanced and innovative machine learning methodologies and analyze data. The postdoctoral researcher will join the groups of T. Mora and A. Walczak, whose
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velocity anomalies (Vp, Vp/Vs). These observations will be quantitatively compared with model predictions to test whether the system is dominated by the geometric interactions of the faults, the rheological
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rate and will receive partial reimbursement for public transportation fares if necessary. Despite the spectacular success of AI-based approaches for protein structure prediction, reliable 3D modeling
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, France to complete the project. Objectives The objective of this PhD project is to develop advanced numerical modelling tools to support the digital twins of refractory materials, with the aim
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption