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of computational, analytical and experimental techniques to investigate potential fuel additives to understand the mechanical, chemical and environmental impacts. We are looking for an enthusiastic and
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in a related discipline. This project would suit motivated graduates from a wide range of STEM backgrounds—including environmental, civil, chemical or mechanical engineering, computer science, robotics
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, engineering (e.g.: mechanical, electrical, electronic, materials) or a related subject. A relevant master’s degree and/or experience in the use of wearable technology will be an advantage but not essential
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
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being recorded. This project aims to uncover the neural mechanisms underpinning communicative challenges in socially anxious individuals. The second PhD candidate will investigate how socially anxious
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need to be enrolled in one of the EPFL doctoral school programs. This is a separate application process that you can start in parallel. We suggest that you apply to the Doctoral Program in Mechanics
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synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
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spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based on the use of state-of-the-art
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Organisation Job description Project and job description Our project will make use sensing technologies (hyperspectral cameras, NIR and Raman sensors), and an edge-compute AI pipeline to sort used
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item