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on the following key questions: Analysing Centralization and Accessibility: Using population data from real world data sources such as cancer registries and data from the central bureau of statistics, the PhD
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page) Full CV Diploma and transcripts of records (BSc and MSc including grade transcripts) Other information to consider: Publications, if any and relevant references and recommendations When assessing
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and/or Python. Experience in, and aptitude for, complex statistical modelling (inc. mixed effects regression models and/or Bayesian statistics). Excellent written and spoken English. Desirable (traits
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Ecology Associated Applied Statistics and Empirical Methods (ASEM) Faculty of Chemistry Faculty programme Chemistry Thematic programme Catalysis for Sustainable Synthesis (CaSuS) Faculty of Geoscience and
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model checking. You should be well versed in basic statistics and practical programming skills is a must. Knowledge about the inner workings of GenAI would be nice but not necessary. You must have a two
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., linear algebra, statistics, optimization, and calculus) is expected, along with programming experience using deep learning frameworks in Python (e.g., PyTorch). While prior knowledge of machine learning
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conducting biodiversity research at different levels. MSc/Diplom (DE) in a field related to ecology (e.g. pollination, chemical or molecular ecology). Strong experience with statistical data analyses
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candidate with: - with an MSc degree in epidemiology, health sciences, public health, (health) economics, econometrics, mathematics, quantitative sociology, (developmental) psychology or a related field
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will investigate localizations of the dynamics in appropriate subsets of the state space, analyzing stability and predictability of trajectories via (moment) Lyapunov exponents and their statistical
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statistical programming languages, such as R or Python; (ideally) has gained some experience in writing scientific publications. Our offer A position for one year, with an extension to a total of four years