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. The functional relevance of these biomarkers will be investigated using both in vitro and in vivo models, as depicted in the publications of team. Selected Publications from the Team 1: Dousset L, et al
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the mechanisms of sorption and diffusion; (iv) to establish relationships between molecular structure and adsorption properties; and finally (v) to combine experiments and simulations to predict the performance
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, resilience and evolution of marine life to develop solid theories and predictive models of the relationships between marine biodiversity and ecosystem functions, which will in turn lead to improved economic
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protein structural insight with hands‑on ML development: adapting and applying state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational
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: Computational, Quantitative, and Predictive Modeling of Root Systems. This position emphasizes integration of phenomics and other -omics data into predictive frameworks. Research areas may include: Structural
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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regimes. This PhD project aims to develop predictive pore network models integrated with thermodynamics and upscaling methods toward reservoir-scale applications. We seek candidates with a strong background
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learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework Simulation and analysis of batch and gradient elution
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—these approaches can recover unmeasured near-wall structures, improve subgrid-scale modelling, and enhance predictive accuracy. Possible project directions include: 1. Reconstructing near-wall velocity fields from
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during these experiments will be used to calibrate a numerical model of PFAS fate in soils. The predictions from this model will then be compared with PFAS concentration measurements in leachate collected