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, complex question several research approaches may be used in various combinations, e.g., statistical, and process-based plant diversity and vegetation modelling, remote sensing, meta-analyses, existing
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, including advanced statistical and methodological knowledge (such as multilevel modelling, Structural Equation modelling etc.) Basic technical skills and knowledge (e.g., general programming skills in R
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the analytical and statistical skills of the department with an emphasis on e.g. spatial modelling, movement ecology, bioacoustics and deep learning. To play a central role in contributing to the department’s
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assignments. We are seeking candidates proficient in applied statistics, data management, and experienced in SAS and R programming. As part of this position, you will collaborate with researchers from
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, stakeholders, and managers) Experience in data analysis and statistical tools Preferable experience with advisory work Who we are The Department of Ecoscience is engaged in research programs and advisory work
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genetics, animal breeding, statistics, or a related field. Proficiency in statistical analysis of genetic data related to insect breeding and experience in developing insect breeding strategy. Experience
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competitive national and international research applications Experience in planning and conducting field-work Experience in advanced statistical analysis of results Experience with analyzing drone images and
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The National Centre for Register-based Research (NCRR) seeks to hire one or more research assistants for projects focused on psychiatric epidemiology and statistical genetics. NCRR is part of Aarhus
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University, a leading international research group investigating psychiatric epidemiology and statistical genetics. The Centre has a strong track record in collaboration with other Danish researchers and with
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. Methodologically, candidates should hold strong quantitative skills and excellent command of statistical programming languages (e.g., Stata or R) and have experience with using research designs for causal inference