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interests center around the identification of small molecules using mass spectrometry data, and the use of language models to predict the existence of undiscovered small molecules that are likely to be
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and refine the RG-based model to enhance its biological interpretability and robustness across different tumor types; to extend the model to simulate and predict solid tumor response to innovative
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Description Whether behaving as a solid, fluid or gas, powder is a state of matter that is difficult to model on a large scale, specifically in industrial equipment. The sizing of powder agitation devices and
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anatomical modeling with mechanistic physiologically based pharmacokinetic (PBPK) models, enabling simulation of radiopharmaceutical distribution at sub-compartment level. By integrating high-resolution
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short- and long-term demand prediction, renewable generation forecasting (solar, wind, hydro) under uncertainty, spatiotemporal modeling for distributed energy systems, energy markets, transfer learning
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-the-loop control for extreme robotics applications, including high performance algorithms for 3D perception, model predictive control, reinforcement learning, generative AI, and simulation and virtual
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and temporal patterns from multisource data, spatiotemporal data analysis and mining and model learning and physical parameter prediction. Responsibilities consist of conducting computer modeling
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consumer is exposed to Lineage I or Lineage II of L. monocytogenes would be different. Combined with their differing virulence (i.e., dose-response models), this would impact risk assessments in a way that
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optimisation. State-of-the-art digital models and AI tools that incorporate machine learning could enable predictions of the dry fibre forming that are subsequently used as input into the RTM process model
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functioning will be supported by several EU projects (participation to congress etc..). - main mission: He/she will develop a new generation of predictive models incorporating abundance distribution across size