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. Work tasks Statistical analyses of biological data, primarily for herring and cyprinids, collected from the commercial fishery as well as from scientific surveys in the Baltic Sea to assess spatial
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, genotyping, immunohistochemistry, RNA in situ hybridization and statistical analyses. Qualifications The ideal candidate should have a PhD in molecular or developmental biology, neurosciences, photoreceptor
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computational and statistical methods, with demonstrated experience in reproducible and scalable bioinformatics environments. The applicant will work in close collaboration with other Engblom lab team members who
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Experience with satellite remote sensing and geographic data Excellent English communication skills (spoken and written) Highly desirable skills: Experience in GIS, remote sensing, statistical analysis and
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Inference for Statistics, Social, and Biomedical Sciences, or Pearl, 2016, Causal Inference in Statistics) Training deep learning models at scale on compute clusters. The candidate should have a proven track
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the position Experience with independent planning, execution and evaluation of experiments, including statistical analysis Familiarity with theory and techniques of chemical analyses or metabolomics is required
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computational and statistical methods, with demonstrated experience in reproducible and scalable bioinformatics environments. The applicant will work in close collaboration with other Engblom lab team members who
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. Project overview The project involves applying advanced statistical analysis, machine learning techniques, and modeling approaches such as agent-based modeling to analyze diverse climate and socioeconomic
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at the Department of Public Health and Clinical Medicine at Umeå University. We are looking for someone with an interest in statistical methods who wants to be involved in developing our research environment
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research experience in phylogenomics. You should have a strong background in bioinformatics, statistical phylogenetics and comparative genomics. Previous experience of working with de novo assemblies and