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analysis, focused on selected electrodes or brain regions. We would like to investigate how graph deep learning models can be designed to capture dynamics in brain signals for the accurate detection, and how
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of eligible participants, clinical trials in rare diseases often cannot achieve the standard 80% or 90% power requirements, alongside a 5% type I error rate, in the final analysis. There is widespread
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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(e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) Willingness to adapt and work across different disciplines Ability to work independently and cooperatively Commitment
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, hierarchical and composable analysis and design is needed. The project will leverage tools from contract-based design theory to formalise such security and performance guarantees at multiple resolutions
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permanent magnet electrical machines are pushing performance boundaries. A major challenge is the structural integrity of the carbon-fibre reinforced polymer (CFRP) sleeves used to contain the rotor’s magnets
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. The start date is October 2025. Are you passionate about applying computational science to real-world engineering problems? Do you want to develop digital twins of materials that can predict performance and
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Job Purpose The successful candidate will contribute to a project focusing on remote RF Sensing and its applications in healthcare involving technologies for both sensing and communications, under
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the field of Computational Morphodynamics in plants. The work will be within the ERC-funded project RESYDE (https://resydeproject.org ) with the aim of building a virtual flower using multi-level data and