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or as soon as possible thereafter. The successful candidate will be working on statistical methods, scientific programming and analysis projects together with external collaborators and together
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PhD scholarship in financial influence at the Faculty of Humanities, University of Copenhagen (UCPH)
analyses is a requirement. Applicants must possess skills in written and spoken academic English at a high level. Fluency in Danish language is required for conducting fieldwork and analysis of data
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, experience with plant molecular biology and biochemistry such as western blots, immunoprecipitation, protein kinase assays, plant immune assays; data analysis using R and Python, multivariate statistics
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outline how the applicant envisions the empirical research including reflections on scope, sample, method(s) of data collection and method(s) of analysis. Qualification requirements and assessment criteria
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on methods and concepts. The project: Philosophical Perspectives on Animal Models in Translational Neuroscience The aim of the PhD project is to provide a practice-oriented philosophical analysis of the role
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of the department: Algebraic and arithmetic geometry, applied algebra, combinatorics, geometric group theory, geometry and geometric analysis, Lie theory, representation theory, number theory, and topology, as
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developmentof new experimental setups, including complex deuteration methods, and advanced data analysis combined with simulations and mathematical modeling. The position in anchored at the Department of Food
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an advantage, as will experience with omics techniques such as transcriptomics, metabolomics, proteomics and the associated treatment and interpretation of data. Data analysis and statistical skills will also be
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on how accountability could be ensured for victims of mercenarism. MERCURY focuses on this accountability void, combining (1) cutting-edge, data-driven mapping and analysis of mercenary operations with (2
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collaboration with survey providers in Denmark and Germany. Analyzing survey data using quantitative methods such as conjoint analysis, regression modeling, and causal inference techniques. Writing academic