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deal with numerical and/or categorical data [e.g. Klassen et al., 2018], textual data [e.g Assael et al., 2022], images [e.g. Horache et al., 2021 and geospatial data [Ramazzotti, 2020]. Applications
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of the moving sources, and directionality of the DAS measurements, make the use of machine learning techniques very appealing. The doctoral student will propose deep learning methods for source separation of DAS
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Nature Careers | Port Saint Louis du Rhone, Provence Alpes Cote d Azur | France | about 21 hours ago
and and experience in computational methods applied to structural biology. A strong publication track record.
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your mission will be to carry out multi- and hyper-spectral measurements
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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at the interface of machine learning and computational neuroscience. The candidate will be part of the COATI joint team between INRIA d’Université Côte d’Azur and the I3S Laboratory. Project The candidate should
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processes and natural environments, making this research both ubiquitous and interdisciplinary. The increasing availability of experimental and production data, requires new computational methods
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degree in computer science and: You have a good knowledge of C++ You have skills in software engineering. You are familiar with common development environments and associated tools Knowledge of parallel
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Discrete geometric representations such as meshes are a crucial part of engineering simulation pipelines. The success and fidelity of numerical methods heavily depend on the accurate representation
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Master/engineer degree in computer science, applied mathematics, data science with background in image processing, imaging inverse problems, deep learning and optimisation. Good coding skills for numerical