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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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and and experience in computational methods applied to structural biology. A strong publication track record.
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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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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
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Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time-series analysis of PET-CT data o Develop a methodological formalism for integrating
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the geometrical variability in imaging data. During the project, the candidate will: o Exploit an existing clinical database containing numerous follow-up FDG/PET exams o Develop modeling methods for the time