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this goal, we will combine new datasets, a deeper process-understanding, sophisticated models and improved experimental design. As part of CLARiTy, this PhD will focus improving the representation of tropical
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. This is a 3.5-year PhD position based in the Structured Light Lab, within the Department of Physics and Astronomy at the University of Exeter (Streatham campus, Exeter). The Structured Light Lab is led by
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This PhD “Novel use of AI in Marine Monitoring” will focus on marine monitoring in NEOM Nature Reserve (NNR) within northern Saudi Arabia. The PhD will develop techniques for automation in surveying
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are fundamentally limited by a "one model for one task" design philosophy. This approach incurs prohibitive engineering costs and yields brittle solutions with poor generalisation to new network conditions, trapping
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Change, University of Exeter The University of Exeter invites applications for a PhD studentship in geospatial ecology starting from April 2026 onwards. The student will join the Terrestrial Ecosystem
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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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PhD Studentship: Subcortical brain development and disorders, funded PhD at the University of Exeter
and coordinate motor responses. Despite its importance, our understanding of its development and how it may be disrupted in neurodevelopmental disorders is still largely unknown. This funded PhD project
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Surcharge Relocation expenses associated with moving to the UK to undertake a PhD. Applicants should ensure they have sufficient funds to meet these costs before applying. Funding Comment UK tuition fees and
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shipping and changing regulation now makes the ship design problem more challenging than ever. This fully-funded PhD studentship in partnership with Mari-UK, University of Newcastle and BMT Ltd, will
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined