304 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" research jobs in Sweden
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machine learning on large epidemiological cohorts, diet and health data analysis of omics data (metabolomics, proteomics, microbiome, etc.) development of predictive models and digital decision-support
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the new Chalmers Physics and Astronomy Department. The new department will provide a creative and innovative environment for research, learning and outreach. The research team and the AoP division
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aspects of both. The first direction concerns the data-driven discovery of dynamical rules underlying developmental trajectories. The aim is to develop and analyze quantitative frameworks that learn
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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For more information about Precillience, see: https://www.precilience.eu Read more about our benefits and working at SLU by visiting https://www.slu.se/en/about-slu/work-at-slu/ Location Uppsala Type
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data using multivariate statistics and machine-learning–assisted approaches, in close interaction with data science collaborators Active collaboration across disciplines spanning spectroscopy, soft
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. 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/?rmpage=job&rmjob=14445&rmlang
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. Where to apply Website https://lu.varbi.com/en/what:job/jobID:906273/type:job/where:39/apply:1 Requirements Research FieldMedical sciencesEducation LevelPhD or equivalent Research FieldMedical
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Website https://uu.varbi.com/en/what:job/jobID:911571/type:job/where:39/apply:1 Requirements Research FieldTechnologyEducation LevelPhD or equivalent Research FieldTechnologyYears of Research Experience4
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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