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part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This position is
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biopsychosocial perspective to the causal inference of stress on (mental) health and wellbeing. Supervisors: Renate Reniers (r.l.e.p.reniers@bham.ac.uk ). Funding notes: This is a PhD studentship with the Midlands
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for environmental epidemiology (Epi, survival, sf, gstat, mgcv) and causal inference (dagitty, MatchIt), as well as contributing to reproducible, scalable data pipelines. Machine learning integration: Exploring ML
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is an AI-based technique that supports imitation of the preferred system behaviour by using its behavioural history. It helps in the inference of the reward values by taking the observed history
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of interest, which will be used to compute the volumetric part of eigenstrains. Second, the bicrystallography will permits from the atomic structure of the crystals to infer the deviatoric part of
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resources like UK Biobank. Advanced Techniques: Applying causal inference frameworks, unsupervised learning for phenotype discovery, and population-specific Genome-Wide Association Studies (GWAS). Ethical
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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inferred from seismic data. A robust methodology for predicting sediment properties from geophysical data could reduce the number of boreholes required before investment decisions, enabling faster and more
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inferred from seismic data. A robust methodology for predicting sediment properties from geophysical data could reduce the number of boreholes required before investment decisions, enabling faster and more