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then be fed into process-level flowsheet models. The UK-National Nuclear Laboratory (UK-NNL) is supporting this project. In addition to acquiring training in computational modelling and theory
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable
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models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
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of Computer Science and Technology at the University of Cambridge, UK. This position is part of a broader effort to advance fundamental research in classical and quantum complexity theory. The successful candidate will
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Primary Supervisor: Dr Jonathan Kirby Model theory is traditionally done with “classical first-order logic”, the logic which allows unlimited use of the operators AND, OR, NOT, with the EXISTS and
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develop knowledge-grounded reasoning methods that connect salient nucleotides, motifs, genes and regulatory elements to biological annotations, ontologies, knowledge graphs and literature-derived evidence
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deep-learning and 3D computer-vision models that detect features while representing a distribution of plausible interpretations. Encode geological relationships in a knowledge graph that stores
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Primary Supervisor: Dr. Christopher Birkbeck This PhD project, based at our Norwich campus, offers a unique opportunity to work at the confluence of number theory, representation theory, and formal
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movement, gaze and interaction dynamics; encoding simulated cognition using graph- and transformer-based methods; training and evaluating models on publicly available and ethically collected clinical or sub
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spectral analysis, entropy-based metrics, graph representations of cardiac conduction, and supervised, unsupervised, and deep learning approaches for classification of abnormal electrical activity within