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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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Research, University of Copenhagen, Denmark. Candidates should have a strong background in (genetic) epidemiology, bioinformatics, statistical genetics, data science, machine learning, or a closely related
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analytical skills in core economic theory and quantitative methods; strong analytical skills with statistic methods and farm surveys. ability to draft scientific papers in English and effective oral
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for: Leading the process of integrating data from Project FOREVER with data from Statistics Denmark. Investigating how self-reported glaucoma aligns with clinically verified glaucoma. Validate disease case
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at different scales. Additional relevant experience includes the development and recovery of forest ecosystems following land-use change, statistical analyses, data science. 2) innovative methods for monitoring
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), laboratorieøvelser (skema A) 75 timer Nanokvant (skema A) 125 timer Datalogi for fysikere (DatF) (skema B) 120 timer Fødevarefysik (skema B, kurset er for fødevare-studerende) 120 timer Applied Statistics – from Data
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PhD fellowship in conservation science - developing and validating indicators of ecosystem integrity
experience in field, database and GIS work on biodiversity and ecosystem processes, such as ecohydrology, nutrient dynamics and carbon pools, as well as statistical modelling will be considered assets.Fluency
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in geology, genetics, ecology, evolutionary biology, computer science, statistics, and more. During onboarding, there will be particular emphasis on facilitating integration in the Center and the
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digitisation statistics Manage volunteers Work with a variety of internal and external staff, acting as an advocate for digitisation and helping ensure a positive, supportive and productive work environment Help
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skills written and spoken Desirable experience and skills: Experience in teaching and supervising undergraduate and MSc students Experience in statistical analysis (for example Mixed-Effects models and/or