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application-oriented research that unlocks the potential of data through rigorous analysis – advancing solutions in societally relevant domains. Your profile Master’s degree in Statistics, Industrial
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) Documented record of advanced quantitative methods skills in R and Python, specifically Experience with GIS and spatial data analysis Experience with natural language processing or text-as-data approaches
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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two
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grasp of spatial statistical methods in R is considered highly advantageous. Excellent communication skills are required, with proficiency in English and preferably one of the Scandinavian languages
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, however, these may not always be able to provide the spatial and/or temporal coverage that is required. In such cases, information from ground measurements can be combined with information from other
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-scale job ads datasets, spatial datasets, patents). Conduct data analysis using econometric and statistical tools. Excellent knowledge of R is expected. Good knowledge of Python, experience with modern
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afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq, spatial transcriptomics). You like to work in a diverse setting and
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for estimating sustainable city size by identifying key determinants (e.g., accessibility, agglomeration, emissions, equity), testing spatial boundary sensitivity, and broadening sustainability
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, stress or endocannabinoid system. You like a challenge and are not afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq
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. In particular, the research project will focus on inferring trajectories from spatial transcriptomics data modelling at the same time the cells evolution in gene expression and in space. Required