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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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performance. These models will be combined into a multi-physics framework capable of predicting reactor behaviour during normal operation and transient conditions. You will validate simulation results using
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and strong interest in programing, modeling, and computer simulations. Knowledge in statistical mechanics, active and living matter. High degree of independence and commitment. Very good command
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sphere of influence: We seek a highly motivated PhD student, who will carry out Lagrangian transport model simulations with FLEXPART, of the emissions originating from rocket launches as well as re
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topography, meteorology, and historical fire events. Ultimately, you will use this model to simulate various land management scenarios and mitigation measures, translating complex spatial data
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modelling is an advantage. Strong quantitative skills and interest in statistical modelling, or simulation approaches. Experience with R, STATA, Python or similar software. Ability to work independently and
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actors. Special emphasis is placed on numerical simulation to reduce the required experimental effort, but the validation of the models in laboratory setups will remain essential. Your role The research
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advanced multiscale mechanical–transport modelling with experimental validation to quantify and predict the structural evolution of both the selective skin layer and the polymeric support layer under
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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 2 months ago
out research duties within the project “FibroRevert - Modeling and targeting cell-extracellular matrix communication to revert tissue fibrosis”, GA 101221669, funded by the European Research Council