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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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early ‘prodromal’ stages) is yet to be established in large community settings. This PhD project will examine the effectiveness of AI-based analysis of eye images in predicting cognitive/neurodegenerative
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interviews to assess health behaviours. Experience in a variety of qualitative analysis techniques such as thematic analysis and Interpretative Phenomenological Analysis (IPA), in addition to other mixed
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and networking. They will be working within a vibrant community of research degree students and researchers at both institutes. The post-holder must have a relevant first (or postgraduate) degree in
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framework will be used with advanced causal inference methods – including inverse probability weighting to construct a valid comparison group. The analysis will use the potential outcomes approach to address
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liquid nitrogen and liquid helium sample environments and integrating electrical biasing setups in the microscope to study ferroelectric materials in situ. Data Acquisition and Analysis: Process and
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) under the supervision of Dr. Elina Spyrou . Summary of Project: Power systems are at the core of the transition to net-zero energy systems, and they have to transform in two ways. First, their generation
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interventions across wastewater treatment operations. CVORR incorporates environmental, economic, technical, infrastructural, policy, and social dimensions to enable systemic and context-sensitive decision-making
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will