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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
investigate the large area production of graphene, BN, MoS2 and other layered materials, optimize their transfer process in view of their application in energy, electronics and photonics. This will include
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hole or neutron star orbiting at a large distance from a ‘normal’ companion star. This is a very long-lived state which accounts for the vast majority of the millions of binary systems harbouring neutron
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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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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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effective delivery of expertise, equipment, and medical resources in response to complex and large-scale emergencies across the United Kingdom. In its initial phase, the research will examine past and
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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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opportunity for a motivated scientist to unpick the impact of host factors on tumour structure. To lead this research as a doctoral student you will be passionate about using large-scale data to address
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-free stipend only (set by the UKVI, £20,780 for 2025/26). Killer waves or extreme waves are large (> 20 meters tall) and unpredictable surface waves that can be extremely dangerous to ships and other
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. The student will perform ‘big data’ analysis of patient cohorts including time-based evaluation of the impact of introducing CT-FFR as a national health intervention into a healthcare system. Exploratory
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of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging