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, optimization, dynamic systems, decision theory, Bayesian inference) ● Is motivated to apply these methods to ecological, evolutionary, and conservation systems; ● Is comfortable with uncertainty, modeling
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been directly observed in planet forming discs around young stars (protoplanetary discs) and is inferred to be occurring around black hole discs. My research projects use a combination of 3D and 1D
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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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. Quantitative, computational, or mixed-method approaches are particularly encouraged, including but not limited to geospatial analysis, machine learning, predictive modelling, and causal inference techniques
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the student to guide the research, but preliminary ideas include: Exploring whether suggested discharge limits derived from single organism experiments are protective of microbial communities; Using Bayesian
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-edge methodological expertise in causal inference and artificial intelligence methods for epidemiology and biostatistics. BIPS is a member of the Leibniz Association, consisting of 96 German research
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, the construction of the geological model remains a significant challenge. The geological model is key because it constrains the volumes and data used when inferring the mineral resources, which in turn inform
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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how well reported dynamics of decline in rare plant species align with inferred trajectories of effective population size. Conversely, the project will also address whether habitat and species
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop