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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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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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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
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truly interdisciplinary environment that conduct pioneered investigations in nanomedicine. We are active in: Provide key contributions to novel personalized strategies of particles design for drug
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essential, while experience with machine learning is advantageous but not strictly required. Excellent English skills, both in verbal and written communication, are required for the project. We are looking
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-based materials and an application in agriculture, health and textiles, in order to identify application fields with high environmental sustainability potentials. This PhD position is part of the project
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of Zurich and Wageningen University & Research. The four-year STEPS project focusses on developing data-driven and machine learning methods to monitor CO2 and NOx emissions using the upcoming satellite