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measurements; Modeling of indoor pollutant emissions, transport, transformation, and exposure, ranging from mechanistic or mass-balance approaches to more advanced computational models; and Related topics in
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other air pollutants, including source-resolved measurements; Modeling of indoor pollutant emissions, transport, transformation, and exposure, ranging from mechanistic or mass-balance approaches to more
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project The Chair of Numerical Modelling and Simulation at EPFL is dedicated to the design and analysis of numerical algorithms for partial differential equations. Our research is oriented towards
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provable guarantees AI-based cybersecurity: applying learning and AI-assisted techniques to network security, e.g., automata learning from security logs, validation of protocol models, and verified defensive
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modeling Contribute to project planning, risk mitigation, publications, conference presentations and the dissemination of results Profile A PhD in experimental atomic, molecular or optical physics, physical
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to the mathematics of plasmas, with a focus on kinetic theory and PDEs. - Specific topics include stability, long-time behaviour, and mathematical properties of magnetised plasma models (such as magnetized
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at the neuro-immune interface Experimental approaches include in vivo tumour models, conditional mouse genetics, neuronal tracing, genetic and viral manipulation of neuronal populations, immune-cell depletion
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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London, and with a strong team of postdoctoral researchers and PhD students Generous access to frontier AI models and high-performance compute for proof-assistant workloads Funded travel for collaboration
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electrical measurements, or vacuum/cryogenic instrumentation Strong analytical and problem-solving skills and the ability to independently troubleshoot complex experimental problems Independent, creative and