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) epidemiology, bioinformatics, statistical genetics, data science, machine learning, or a closely related discipline, and have experience with research in obesity, diabetes, cardiovascular disease, and/or life
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on financial and employee stress using Denmark Statistics databases. The starting date is 1.10.2024 or subject to mutual agreement. The research project The project is about employee stress in various
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statistical modeling and methods development to experimental hypothesis evaluation and clinical translation. There are also close collaborations with the wet-lab and clinical groups at the department ( https
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Vesteghem, [email protected] , tel.+45 6166 7681. Qualifications You should hold a PhD degree in computer science, statistics, physics, mathematics, engineering, or a field of science relating to data
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working with behavioral and preference models, large-scale surveys, data management, and multivariate statistical analysis. The job The research will be carried out in the context of two projects, but
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uni- and multivariate statistical analyses Who we are At the Department of Agroecology, our main goal is to contribute to sustainable solutions to some of the world’s biggest problems within the areas
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disciplines mathematics, statistics, computer science, and engineering. We conduct research, teaching and innovation of high international standard – producing new knowledge and technology-based solutions
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countries. The ranking is based on recognized EU data sources such as CORDIS and Erasmus+ as well as publication and citation statistics based on the Scopus database, and patent statistics from the European
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management, visualization, and interpretation, including proficiency in statistical analysis What we offer in return DTU is a leading technical university globally recognized for the excellence of its research
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inorganic geochemistry, are also invited to apply. Experience with multi-proxy studies, biomarker analyses, carbon chemistry and/or numerical/statistical data treatment will be considered favorably. Excellent