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, advanced materials, and innovative concrete strengthening solutions. Our work combines experimental investigations, analytical modelling, and numerical simulations to develop and validate novel reinforcement
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refrigerator (CNDR), which would allow ultra-low temperature researchers to take full advantage of cryogen-free technology. - Based on the existing codes, propose a route for a software versatile enough to model
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About the Opportunity Job Summary The Copos Group works on computational and mathematical theoretical models with direct applications to several open problems in cell biology. We are specifically
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inventory; analysing spatial data obtained during fieldwork; creating data visualisations, predictive models and advanced data analyses based on results of analyses of different types of findings; assisting
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for the agricultural transition. However, numerous farmers in the Netherlands and abroad demonstrate that successful regenerative business cases are possible. Their models often rely on strongly reduced input costs
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of electrophysiology (in vivo or in vitro), stereotactic surgery, behavioural experiments (especially in mouse models). • High self-motivation and good teamwork skills, as well as a willingness to work with animals
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matter physics * Strong experience in out-of-equilibrium physics and techniques in condensed matter physics. * Experience with computational and theoretical simulation and modeling techniques in
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numerical validation of error bounds for model-order reduction based on optimal transport, in the context of electronic structure calculations. The postdoctoral researcher will be assigned to the Besançon
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: applied mathematics, mathematical physics, engineering mathematics, theoretical physics, electromagnetic theory, electromagnetic modeling, microwave and RF engineering, electromagnetics Appl Deadline: 2026
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., PyTorch, JAX, TensorFlow, R, Bash). Strong publication record in top-tier venues (e.g., NeurIPS, ICML, ICLR, Nature Methods, Nature Biotech). Background in at least one of the following: generative modeling