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of interest, e.g. the spectral density of strongly correlated models, as measured in ARPES. • The mathematics of the approach will require a lot of innovative developments too. In the only published article
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regenerative capacity of liver tissue. Your work will involve the use of research animals to investigate cell-cell interactions during regenerative processes and to developed new in vitro organoid models. You
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, plus preparing modelling outputs that can be shared with project’s partners. The successful candidate will work with academic and industrial project partners to generate simulation and experimental
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
Foundation Model for Continuum Dynamics*. November 19, 2025. https://doi.org/10.48550/arXiv.2511.15684 Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. *The Frontier of Simulation-Based Inference*. Colloquium
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(materials, scenarios) - Translation of local knowledge into simulation rules - Facilitation or co-facilitation of model testing sessions - Collection of feedback for prototype iterations. Analysis and
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. The PhD student is expected to develop and apply methods for causal inference will be part of the SMARTbiomed Pioneer centre https://SMARTbiomed.dk/about-SMARTbiomed. Project Description The PhD project
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-fidelity modeling approaches combining simulations of different fidelity levels, ranging from RANS calculations to high-fidelity LES, possibly associated with various levels of geometric or physical
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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structures but also free energies. The work sits at the interface of statistical mechanics, molecular simulation, and deep generative modeling, and is embedded in SIMPLAIX, a research initiative on multiscale
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time