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of supporting the selection of aortic valve prosthesis using CFD simulation, machine learning and multi-stage validation, (2024/54/E/ST8/00100) Requirements: PhD in technical sciencesThe mandatory formal
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close
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You hold a PhD in Computer Science, Artificial Intelligence, Applied Mathematics, Electrical Engineering, or a closely related field. You have demonstrated expertise in machine learning and deep
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. The successful candidate will have the responsibility of developing, in collaboration with Dr Whelan and the PhD students, machine learning tools for the handling of the Mauve and MUSE datasets. They will also be
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also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning, computational biology, or a closely related field Strong research track record
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the support of and under the supervision of a Principal Investigator. To become familiar with the publication process. To acquire generic and transferable skills (including project management, business skills
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using
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on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research