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engagement and participatory research with quantitative system modelling of our research group; experience with systems modeling and computer programming (e.g. Python); strong communication and scientific
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related discipline; An interest in the impacts of land use; Experience in handling spatial data; Programming experience in Python, and/or R; Proficiency in English; and The ability to work as part of
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; Knowledge on handling large scale datasets; Programming skills in Python and/or R; familiar with reproducible coding and automated (geospatial) data analysis; Familiarity or interest to dive into
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, Earth Sciences, Civil Engineering, or a related field. You have a strong background in numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD
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assessment. Programming and data-analysis skills in a reproducible scientific workflow, for example using Python or comparable tools, and experience with GIS-based spatial analysis. The ability to work
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professional competence in quantitative and statistical methods, including applied econometrics and panel data analysis, proficiency in a statistical programming language such as R (preferably
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). Experience in multi-modal data integration for prioritizing disease-specific targets: combining quantitative multi-omics approaches (es. RNA-seq, Ribo-seq, proteomics, immunopeptidomics). Strong programming
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transformers, self-supervised learning, foundation models, autoencoders or related architectures; • strong programming skills in Python and experience with a deep-learning framework such as PyTorch
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veterinary parasitology. https://www.eurovetpar.org/residencyprograms Your training will take place within the Faculty of Veterinary Medicine at Utrecht University in collaboration with Ghent University in
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will complete an individual four-year training programme designed to provide broad and in-depth expertise in veterinary parasitology. https://www.eurovetpar.org/residencyprograms Your training