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Research Associates/Assistants to support a diverse portfolio of research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven
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closely related discipline. • Minimum of two years of postdoctoral experience. • Demonstrable expertise in Bayesian inference and probabilistic modelling; experience with Stan, PyMC, NumPyro, Turing
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guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
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and geometric deep learning, or simulation-based inference. We welcome your unique perspective and are eager to learn how your track record, educational vision, and future research goals align with
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(publications, preprints, software, reports). Highly desirable: • Demonstrated experience in inverse problems and/or statistical inference (ideally Bayesian). • Prior experience with satellite remote
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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biostatistical training and skills, including longitudinal and correlated data; familiarity with advanced analytics including machine learning, Bayesian methods, and causal inference also desired. Strong written
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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following: Machine learning/Artificial intelligence Causal inference for observational and interventional studies Bayesian methods Clinical trials methodology Analysis of electronic health records and other
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on the website. The Information is used to track and analyze user behavior, to meet the individual user needs and to deliver targeted advertising. Personvernregler for databehandling: Youtube, Google