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implementation of decentralised personal data governance frameworks? Technology: How can multimodal knowledge graphs in combination with generative AI help individuals govern their data (e.g. within decentralised
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
Overview: Cranfield University invites applications for a fully funded 3-year PhD, supported by the EPSRC DTP and Rolls-Royce. This studentship covers tuition, a tax-free stipend, funding
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address: Dr Fahad Panolan: f.panolan@leeds.ac.uk Project summary The Algorithms group at the University of Leeds (UK) is offering a fully funded 3.5-year PhD studentship on Parameterized Complexity and
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
. This PhD project will tackle that challenge by developing intelligent methods that combine AI techniques such as language models that interpret technical text and knowledge graphs that map engineering
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(coordination) and safety constraints can be intractable. Your work will bridge this gap by providing generalizable, provable design approach that apply across a wide range of networked systems. This 3.5-year PhD
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We are inviting applications for a fully funded 3.5-year PhD Computer Science studentship at the University of Warwick, jointly supported by GlaxoSmithKline (GSK), to work on an ambitious project
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replacement) project on Limits of Symmetric Computation. The position would suit a candidate seeking to obtain a PhD at the Department. The project seeks to investigate lower bounds on symmetric computation in
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replacement) project on Limits of Symmetric Computation. The position would suit a candidate seeking to obtain a PhD at the Department. The project seeks to investigate lower bounds on symmetric computation in
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. Alternative approaches are graph-based molecule reaction space sampling and generative machine learning as they provide a path to new synthetic data that can form the basis for a large-scale database of