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Field
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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/ITS sequencing) to understand how residues drive nutrient cycling and soil health. Parameterise predictive models and translate outputs into practical grower tools, including a fertiliser credit
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correlate the findings with prognostic parameters, refining the prognostic value of the Immunoscore for MuM in order to identify predictive biomarkers and refine TME-based therapies. Project aim: The aim
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an advantage: Deep learning or machine learning Structural bioinformatics Protein structure prediction and modelling Molecular simulations Membrane proteins or membrane biophysics Scientific software development
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neutron stars. However, reliable predictions in regions where experimental data are scarce remain limited by uncertainties in model parameters, functional form, and the extrapolation of calibrated
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federated learning and multimodal deep learning models for healthcare. The project will focus on enabling privacy-preserving learning from distributed healthcare data sources, including longitudinal medical
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
phenomena. New meta-model architectures based on learning may be proposed and tested on complex EDF use cases. However, this is not sufficient: can such a surrogate, learned from simulation data, predict
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mHealth systems integrating smartphones and wireless sensors with signal processing algorithms and artificial intelligence (AI) models for unobtrusive sleep apnea detection, multi-night monitoring
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to schematize and parameterize the subsurface at local to regional scales to assess dike safety using geological and geohydrological data and models, for characteristic fluvial landscapes in the Netherlands. UU
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from extreme weather events such as flash floods, heatwaves, or tropical cyclones. This project aims to combine generative weather prediction models with rare event techniques to generate plausible