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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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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
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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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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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. Alexander Ecker (Institute of Computer Sciences, University of Göttingen). Candidate profile We are looking for a highly motivated candidate with: A strong background in animal behavior, behavioral ecology
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knowledge of Python and FORTRAN programming, UNIX/Linux and shell script Knowledge in dealing with server-based computer structures and high-performance computers (e. g. HLRE at DKRZ) Confident handling