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requires high experimental skills from planning to implementation to data acquisition and processing. You must have extended hands-on experience in at least two of the following areas: molecular beam sources
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requirements are uncovered and different approaches evaluated. This may be a technical, process, or behaviour based intervention. Evaluating the intervention:. The candidate will evaluate the intervention within
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is an irreplaceable material in our modern life, while steel industry accounts for 9% of global anthropogenic CO2 emissions. A range of low-emission steel manufacturing processes is being developed
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letter of "Confirmation of funding" must be attached/uploaded during the application process. Candidates should supply a brief research proposal of 2-4 pages. This should include a brief literature review
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Science, Physics, Mathematics, Robotics, or a related discipline. A strong interest in one or more of the following areas: AI and machine learning, computer vision, signal processing, sensing, robotics, or embedded
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PhD Studentship: Bottom-up Decoding of Protein Conformational Landscapes: from Gas-phase to Solution
process by which proteins traverse the free energy landscape, thus connecting the unfolded and the folded (native) state. Understanding protein folding mechanisms is a key route to better understanding
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conductance (G) and Seebeck coefficient (S) with low thermal conductance (k). Graphene Nanoribbons (GNRs) are promising but currently, designing high-ZT GNRs is a slow, trial-and-error process, as the inverse
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desirable. Funding Support and Research Environment After a suitable candidate is identified, funding will be sought from the University of Nottingham as part of a competitive process, covering home tuition
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work with the UK semiconductor industry. The studentship represent a unique opportunity to be trained in the epitaxy process and to work in an emerging and exciting area of combining AI/machine learning
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Sentinel-1, enable rapid large-scale flood detection but face challenges in generalisation, dependence on labelled data, and representation of hydrological processes. Existing UK operational systems combine