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ability to quantify and model these processes remains limited, contributing to uncertainties in global carbon sink estimates. You will analyze data and samples from past and upcoming expeditions to evaluate
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on skills and interest): Build “incentive-preserving prediction models” for variables with positive global externalities, based on country characteristics (GDP, population density…) Develop procedures
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(including the doctoral dissertation) Strong methodological training in quantitative survey and experimental research (Additional asset: experience with using large language models in surveys) Proficiency in
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of fluid dynamic modeling and the analysis of thermomechanical stresses, you will accompany the entire development process – from the optimization of electrochemical performance to the elaboration
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on the influence of Alzheimer’s disease and aging on changes in cognitive functions in humans. The project combines cutting-edge technologies from genetics, proteomics and statistical modeling to understand
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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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of in vitro and in vivo pre-clinical models, including hiPSC-derived systems The postdoctoral project will combine experimental (wet-lab) and computational (dry lab) approaches Be part of Geman Center
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] Subject Areas: Applied Mathematics, numerical methods, simulation and modelling Appl Deadline: 2025/05/31 11:59PM (accepting applications posted 2025/02/13) Position Description: Position Description
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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling
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-dimensional cell cultures are important to enable realistic cell environments for disease modeling and to analyze cell interactions. This position will address the question of how one can develop minimally