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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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include developing new automated program verification techniques specifically for such programs, as well as specification inference and fault localisation approaches that would make such verifiers practical
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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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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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algorithms for inference and decision-making by pushing the boundaries of computational techniques. The research emphasizes efficiency in resource and data usage, reducing environmental impact, and ensuring
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close collaboration with industry and the public sector. The primary objective of SURE-AI is to create a new generation of algorithms for inference and decision-making by pushing the boundaries
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methods for load identification and modelling to infer load behaviour from measurements at the grid supply point (GSP). Your work will help determine whether new load types need to be defined in the CLM
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research profile and reputation and that of the University of Glasgow/School/Research Group/area, including establishing and sustaining a track record of independent and joint publications of international
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and as 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