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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team
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; survey design and online experimental methods; quantitative data analysis, preferably including choice modelling, willingness-to-pay analysis, segmentation, multivariate statistics, or related methods
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translating consumer insights into new food products, concepts or experiences; • experience with univariate and multivariate statistical analysis of sensory and consumer data, including the ability
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statistical modeling with clinical insight, aiming to improve risk prediction and inform sex-specific prevention strategies in atrial fibrillation patients. The research will be conducted in close collaboration
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PhD position in environmental toxicology and endocrine disruption: Focus on new endpoints in zebr...
organism Proficiency in laboratory techniques, including OECD fish test protocols, histopathology analysis, and behavior analysis. It is an advantage if the candidate has experience in statistical analysis
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community Performing appropriate statistical presentation of the results to be used in reports We expect At least first year of study at a university or similar education. IT competences A flair for
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in general. You are very well-versed working with data, models, statistics, simulations, and in general quantitative methods. Basic experience with programming (e.g. python) is a requirement, while
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and students with a background in a number of different disciplines, including biology, molecular biology, statistics, chemistry, and computer science. About the research project We are seeking a
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, mathematics, biology, and epidemiology, developing and applying novel statistical methods and deep learning approaches for global health challenges. The group’s research spans disease modelling, genomic
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nitrogen dynamics, and climate change mitigation potentials in agroecosystems. You will be contributing specifically to the area of regional simulation using process-based models and advanced statistical