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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 programming, learning theory, parallel algorithms or quantum computing Research publications in theoretical computer science conferences and journals Experience in teaching Computer Science topics
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and and experience in computational methods applied to structural biology. A strong publication track record.
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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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, dynamic and innovative researcher to integrate our community. The ideal candidate will possess deep expertise in the application of cutting edge computational methods to understand the brain mechanisms
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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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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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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