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Field
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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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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 challenging properties of uncertainty, irregularity and
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hydrodynamics for novel marine vehicles, including large ships and small AUVs and offshore renewable energy systems including offshore wind. You are expected to perform advanced computational fluid dynamics
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contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models, alongside complex drug screening, efficacy
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data analysis, with opportunities to apply your skills to integrating this with other data types (e.g. genomic and transcriptomic). The position is full-time and available for a duration of 4 years
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the research group (e.g., organising our weekly meetings, contributing to our social media accounts, administering internal group information) This is part of your existing profile: Completion
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opportunities, which together make us a large and successful community, which is a key hub of activity within our region and nationally. We would be thrilled if you would consider joining our thriving University
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the operational life of battery-powered devices and reducing the environmental impact of large-scale deployments. Advancements in this area support the development of sustainable technologies across various
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment