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strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian
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Matias, Adrián Villaseñor, Rowena Jacobs Topic 4-Machine Learning for Causal Inference in Health Policy Assessment for Decision Making Supervisors - Dr Julia Hatamyar and Professor Andrea Manca Topic 5
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statistical inference, quantum metrology, and quantum machine learning. The group 7 senior researchers (4 full professors), 5 postdoctoral researchers, and 12 PhD students. Its members have a strong
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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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on health Physics-informed image registration approaches to infer injury mechanisms from longitudinal image data. Experimental characterization of the mechanical properties and damage behaviour in soft
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counterfactual reasoning frameworks that uncover latent mechanisms and enable principled hypothesis testing. Our goal is to advance the theory of representation learning and causal inference in high-dimensional
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Scotland using ecoacoustics - Lead supervisor: Dr Vasilis Louca Harnessing biogeochemical tags and environmental genomics to infer habitat use and age-related movement of an endangered shark: from
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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree (PhD), it is important