311 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs in Sweden
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in English. Mark your application with the reference number below. Closing date for application: 13 April 2026 Reference number: 2464-2026 URL to this page https://web103.reachmee.com/ext/I003/583/main
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–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience collaborating in interdisciplinary research teams A doctoral
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network modelling and machine learning for regulatory inference. - Functional validation of candidate TE‑CREs in spruce using UPSC transformation and somatic embryogenesis pipelines; evaluating drought
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: October 2026 Full call details, eligibility criteria, application templates, and a matchmaking platform for identifying potential supervisors are available at: https://www.scilifelab.se/data-driven/ddls
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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
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-emitting devices and other advanced optoelectronic applications. The positions are based in the Green Nanodots Group (https://www.umu.se/en/research/groups/green-nanodots/ ), Department of Physics, Umeå
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at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala Form of employment: Temporary employment 24 months, with the possibility of extension. Scope: 100% Start date: As agreed
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,” available at https://www.bth.se/english/about-bth/work-at-bth/vacancies . The position requires authorization to work with classified data. Security screening may be conducted on the selected candidate
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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, or related areas, fields or environments. We expect experience and competences in one or more fields of research on late working life; labour markets; public, branch and employer policies; lifelong learning