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education to enable regions to expand quickly and sustainably. In fact, the future is made here. Umeå University is offering a PhD position in Computing Science with a focus on machine learning for graph
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20 Feb 2026 Job Information Organisation/Company Centrale Supelec Department L2S Research Field Engineering » Communication engineering Researcher Profile First Stage Researcher (R1) Positions PhD
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/ ) at the Department of Clinical Microbiology, at Umeå University (https:// www.umu.se/en/department-of-clinical-microbiology/ ), the PhD candidate work in the Marie Skłodowska-Curie (MSCA) Doctoral Network GLYCOCALYX
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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SFI FAST: PhD position in Microstructure/texture evolution during extrusion of scrap-based Aluminium
that you are particularly suitable for a PhD education. You must meet the requirements for admission to the faculty's doctoral program (https://www.ntnu.edu/nv/phd) PLEASE NOTE: For detailed information
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sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in healthcare, mobility, public governance, and healthy living. Each PhD position is embedded in one work package and
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. Strong (inter-)national network in field of application. Experience with high-performance computing (HPC) and large datasets. Experience with machine learning applied to geophysical signals. Experience in
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, network science, and decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en
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acceleration of large-scale machine learning workloads Perform characterization and modeling of electronic and optical devices Develop hardware-aware machine learning models incorporating electronic and optical
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projects in data-driven nutrition, such as: statistical modelling, AI, and machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome