323 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Université de Bordeaux " research jobs in Sweden
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an innovative spirit, in close collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. URL to this page https://www.chalmers.se/en/about
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with external partners. Research activities at the division are organized into four main areas: Electric power systems Power electronics Electric machines High-voltage engineering The strength of our
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in English or Swedish. Closing date for applications: January 12, 2026 Reference number: 6733-2025 URL to this page https://web103.reachmee.com/ext/I003/583/main?site=6&validator
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written in English or Swedish. You should be able to provide two references upon request. Closing date for applications: January 11, 2026 Reference number: 6071-2025 URL to this page https://web103
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collaboration with wider society. Chalmers was founded in 1829 and has the same motto today as it did then: Avancez – forward. URL to this page https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies
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related field and have previous academic experience in machine learning. The candidate should have a strong background in metrology and medical image processing. Active participation and collaboration
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will also use focussed ion beam milling scanning electron microscopy (FIB-SEM) to prepare infected cells for in situ cryo-ET. The resulting tomographic data will be analysed by machine-learning assisted
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with
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: research experience in skin biology, tissue repair, reparative medicine, epigenetics, or RNA biology experience in multi-omics integration, advanced statistics, machine learning, or biological data
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loop/TAD structures. - Perform comparative analyses versus Populus tremula; apply network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce