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from the Netherlands (e.g. SOVON, CBS Statistics Netherlands, Netherlands Institute of Ecology NIOO-KNAW, University of Tilburg, Naturalis Biodiversity Centre) and abroad (Forest Science and Technology
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models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent advancements have improved its economic interpretability and
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Requirements As the ideal candidate for this position, you are enthusiastic and ambitious, an independent thinker, excellent writing skills, and a team player. You hold a PhD in (medical) statistics
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, and has proven experience with managing running field experiments, statistical data analyses, and scientific writing. Knowledge of the study system (salt marshes) is beneficial. Good proficiency
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immortal time bias) and optimization of related epidemiological analyses. Performing statistical analyses of patient-related factors, including germline DNA variation, in relation to bladder cancer outcome
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position in the field of Machine learning meets choice models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent
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languages and statistical software such as Python, R, Stata or SAS; Knowledge of quantitative modeling of financial markets, econometric techniques, machine learning, or quantitative empirical research
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reaching their full potential. In our research team, you proceed with a theory-guided design of dashboards in combination with a student-centered co-design and a thorough statistical evaluation in field
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mobile robotics. Towards agile sensing and inference, a series of original studies will be conducted based on uncertainty-aware kino-dynamic modeling and statistical machine learning. We put equal emphasis
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probabilities Robust stochastic processes Tractable models and decision-making Online/continual learning with evolving data Requirements Specific Requirements Master's degree in Mathematics, Statistics