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-world energy applications, the project aims to better capture the dynamics of urban infrastructures across different spatial and temporal scales, from building-level energy demand to district-scale
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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and policy with systems thinking and practical implementation, always grounded in real-world urban challenges. This PhD position is part of the FRACNET-CITY project, a French-Swiss research
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antimicrobial coatings. This project aims to tackle the growing challenge of infections caused by resistant bacteria through designing innovative, non-toxic, and durable antimicrobial solutions. This highly
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crucial insights. In this project, you will contribute to the development of AI-driven methodologies for experimental fluid mechanics , focusing on: Designing multi-fidelity neural networks for adaptive
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key contributions to novel personalized strategies of particles design for drug delivery, imaging, or diagnosis. Characterize, understand, the interaction of particulate materials with cells or tissues