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models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
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Residency ECVDI- large animal track Faculty: Faculty of Veterinary Medicine Department: Department Clinical Sciences Hours per week: 36 to 40 Application deadline: 3 May 2026 Apply now You will
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., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference to certify performance and
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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and
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. reinforcement learning, Bayesian modelling, or other formal models of decision-making and learning). Furthermore, your suitability is further supported by: a track record of publications in peer-reviewed journals
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partners. In addition, you (co-)lead national-scale applied research projects, for instance by taking a leading role on some scientific objectives of one of SoDa’s major research projects (Macroscope project
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, Bayesian modelling, or other formal models of decision-making and learning). Furthermore, your suitability is further supported by: a track record of publications in peer-reviewed journals; the ability
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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