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Field
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carry out research at the frontier of generative modelling using concepts from statistical physics and neuroscience. For example, we have shown that the phenomenon of symmetry breaking is central to
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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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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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qualifications:- you have completed or are close to completion (about to submit/defend) of your Master in Computer Science, Electrical Engineering, Statistics, Robotics or a related field- strong background in
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a Research Infrastructure? No Offer Description What you will do As a Phd-candidate you will work on a four-year research project in the Methodology and Statistics Unit at Leiden University under
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and Statistics Unit at Leiden University under supervision of Dr. Tom Heyman , Dr. Anna E. van ‘t Veer , Prof.dr. E.M.L. Dusseldorp . In this project you will systematically examine the role, impact
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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