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different conditions using existing software (written in Fortran). Analysis of data using quantitative genetics tools (e.g., calculation and comparison of genetic and phenotypic covariance matrices
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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genomic studies and the analysis of archaic ancestry in present-day and prehistoric humans across the globe. The duties will involve large-scale analyses of genomic datasets, from present-day and
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data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art
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area “statistics to serve society” which focuses on developing statistical methods and software for the analysis of large and complex collections of data. The PhD student(s) is planned to be connected
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with the research group, you will primarily work with planning and carrying out research within the project, with an emphasis on data collection, analysis, and reporting of results. Both qualitative and quantitative
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, integrating microfabrication, cell component and biomaterial incorporation, staining of specific biological features, and computational modelling of intrinsic properties. The evaluation of results and further
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position based at SciLifeLab in Stockholm. The project focus on characterizing phenotypic and genomic variation associated with seasonal camouflage variation in willow grouse (Lagopus lagopus). The analysis
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theoretical knowledge in e.g., neuroscience, cognitive science, psychology or equivalent. Experience of statistics, image analysis, and programming will be viewed favorably. The applicant must be able to work
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developed in our group to accurately capture the coupling between magnetism, lattice vibrations and disorder to search for novel magnetic materials. Your studies will include theoretical analysis