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under uncertainty (Studentship code MSP106) Learning to sample: Meta-optimisation of gradient flows using reinforcement learning (Studentship code MSP107) Dynamic Bayesian modelling of endurance sports
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. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
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these changes affect ecosystem functions. To extend these analyses to new types of data and questions, we develop state-of-the-art hierarchical Bayesian methodology. We also actively apply our research to more
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the next. Your models will first be used to analyze completed experiments and identify trends, and later integrated into active learning and Bayesian optimization frameworks to suggest which experiments
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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chemistry concepts (desirable). Familiarity with chemical or biological databases (e.g., ChEMBL, PubChem, PDB) is a plus. Experience with Bayesian modelling, transfer learning, few-shot learning, or other
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the list of FOUR below when applying. Students will be shortlisted for interview across the four projects and will be required to give a short presentation on why they have chosen that project and why
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using MRI scans. DC1 will extend this framework to regional normative models using Bayesian regression and Generalized Additive Model for Location, Scale and Shape (GAMLSS) to derive age- and region
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the study of plant-plant or plant-invertebrate interactions Experience in nematology or nematological methods Strong quantitative skills (e.g., generalized linear mixed models, permutational methods, Bayesian
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree