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technologies (fiber-optic sensors, DIC), and computer science (machine learning tools) in collaboration with de department of Physics. The aim of the BriCE project is to develop a novel bridge monitoring
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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. Skills in computational modelling or machine learning applied to brain signals are an asset. We are looking for a highly motivated, rigorous and curious researcher who is ready to invest themselves in a
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applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department, e.g. seminars, workshops and schools organised by the members
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and optimize their properties for neuromorphic computing through combined electrical and MOKE measurements, and train them to achieve artificial intelligence tasks. - Micromagnetic simulations - machine
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lie at the crossroads of multiple disciplines and involve expertise in optics, electronics, image and data processing (including machine learning), photophysics, chemistry and biology. The position is
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expertise or interdisciplinary experience is a major asset. Scientific skills - In-depth knowledge of teaching strategies, learning models, and educational technology. - Proficiency in the psychology of well
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collaboration between the Exa-SofT and the Exa-DI projects and better support multi-linear algebra and tensor contractions in exascale CSE applications and Machine Learning. As part of the collaborative process
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project will have additional specific requirements that candidates have to fulfill, be sure to check what these are before you apply. As a research fellow at the AMBER programme, you will acquire
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of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for