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statistical methods to estimate the use, effects, and risks of medications based on secondary data and to estimate the progress as well as the survival of cancer. The unit has extensive statistical expertise
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include application of process-based models (e.g., CANDY, DayCent, LDNDC, Daisy) to model within-field N-fluxes (e.g., N2O-losses, NO3-leaching, N-mineralization) support model parametrization, estimate N
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta