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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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to galaxy evolution and observations of galactic magnetic fields. Experience with Bayesian statistics is advantageous. Strong verbal and written communication skills in English are required. To qualify
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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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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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processing Programming skills (e.g. Python or MATLAB) Statistical analysis A strong publication record * The date shown in your doctoral degree certificate is the date we use, as this is the date you have met
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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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statistics. Required qualifications and skills: Solid understanding of maritime transport systems and risk assessment. Proven programming skills (to be demonstrated prior to starting the position). Excellent
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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
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model design and analysis as well as statistical model parametrization and validation techniques. This Postdoc position is part of a five-year research program funded by the Wallenberg Foundation, aimed
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Mathematics and Mathematical Statistics seeks a postdoctoral researcher within geometric deep