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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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preferably has strong programming skills and experience with the modeling and simulations of fluid or solid mechanics or ice sheet flow and deformation (for example by use of finite element/volume methods
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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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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
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completion). Expertise in qualitative research methods, particularly process-tracing, elite interviews, and comparative case study analysis. Strong knowledge of business power, regulatory governance, and
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programmers. You will also work closely together with course teams to develop data generation, data analysis, modeling, simulation, and machine learning workflows as well as develop custom data science-related
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pipelines, as well as processing and analysis of different data modalities. You will have the opportunity to develop data science methodologies. The position involves interdisciplinary research through
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scientific publications, incl. papers, articles and books, must be written in Danish, Swedish, Norwegian or English. The application MUST include the following 8 elements: Application / letter of motivation
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experience in forest ecology, silviculture, or forest management, preferably with a focus on close-to-nature or climate-resilient forestry. Proven skills in data sampling, statistical analysis, and scientific