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multi‑omics data. You will also partner with AI experts to integrate predictive models and advanced analytics into omics workflows. You will work in an expanding team led by Dr. Masoomeh Rahimpour
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the changing climate. The appointee will work in the research team supervised by the Associate Director of Research, on projects that include the prediction of flooding in coastal areas, wave runup and coastal
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perturbations. The numerical predictions will be systematically compared with available experimental data from IRPHE to assess accuracy and refine the model, ultimately leading to a validated numerical tool
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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new vegetation model. The new EEO-based vegetation model should then also be used to predict future transitions and biome shifts to ultimately answer the question to what extent C4 grasslands
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these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and
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on the vision of developing multi-level thrombosis risk prediction models, from cellular dynamics to organ-level hemodynamics. The network integratesin silico, in vitro, and in vivo approaches to understand
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with The School of Natural Sciences and the Discipline of Geology, seek to appoint an AIB/E3 Assistant Professor in the area of Earth System Modelling. More specifically, the successful candidate will utilize
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-package of the ChiExCo program, which aims to develop a reliable computational protocol to predict, for organic chromophores, both chirality quantifying factors (gabs and glum) resulting from excitonic
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, “Time-Varying Operator-Theoretic Framework for Tipping Point Prediction” (PI: Prof. Sho Shirasaka) in the JST PRESTO research area “Exploration of New Science Using Mathematics to Predict and Control