16 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" Postdoctoral positions at Umeå University in Sweden
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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also try going to the Startpage Technical details Code: 500 About http error codes Server: UMU-WEBSRV05 IP: 172.18.132.5 Time: 2025-11-14 06:08:30
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396). The employment is fulltime for three years. The deadline for applications is March 26, 2026 and the
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trustworthiness modeling on multimodal data and machine learning models. The Department of Computing Science has been growing rapidly in recent years, with a focus on creating an inclusive and bottom-up driven
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support, among other benefits. See more information at: https://www.umu.se/en/department-of-computing-science/. You will research in collaboration with the Associate Professor Zoe Falomir. Interested
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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. Prof. Silvia Remeseiro, MTB / WCMM, via silvia.remeseiro@umu.se More information aobut the research in Remeseiro’s group is available through the following websites: https://www.umu.se/institutionen
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conducted in the SYMBIO group (https://www.umu.se/en/research/groups/symbio/), which is a very active research group conducting translational research in the medical area, with funding from e.g. The Swedish
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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