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analysis of spatial-omics and imaging data, developing a framework for simulations, simulating epidemics on social networks, building generative models to improve computation time for simulations, and other
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to the Institute’s needs. Requirements for candidates: Proven experience in advanced hydrological modeling (e.g., conceptual or physically based spatially distributed models), particularly with SWAT+, SWAT-MODFLOW and
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. The main activities include: (i) the experimental implementation and characterisation (spectral, spatial and intensity noise) of frequency conversion devices using chalcogenide components/materials (crystals
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intervention systems; digital healthcare systems; real-time physiological monitoring technologies; multimodal data integration and analysis for imaging and sensing. Human Centered Models and Systems: human stem
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PhD researcher in AI:GENOMIX, you will contribute to next-generation models that rethink polygenic prediction and support the future of precision medicine. AI:X is an ambitious initiative at Aalborg
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. This data will be used as input in climate models to ultimately propose a spatial characterization of bike paths. The methodology will be tested in several areas in France and Switzerland and discussed with
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Lab aims to reveal how metabolism, oxidative stress, and cell death converge to shape cell fate. For more information about the Henry Lab, please visit: https://thehenrylab.squarespace.com/ For more
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. The PhD (M/F), to be recruited in the context of the ERC StG MULTI-viewCELL, will be working on the development of a new method combining machine learning and biophysical modelling to model embryo
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to sign the contract. More information is available on: https://www.dges.gov.pt/pt/pagina/reconhecimento . Workplan and the objectives to achieve: This work aims to develop a numerical approach based on
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biochemical models, data assimilation, spatial analysis and GIS approaches. • Programing skills (e.g. R or Python) for data manipulation and visualisation, and to perform statistical analysis (e.g. mixed models