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development, the studentship will provide exposure to the challenges and opportunities associated with translation of long-acting technologies towards clinical implementation. This studentship is closely linked
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) to work on the development of an Amazonian Early Warning System (AmEWS) integrating Earth Observation data, process-based ecosystem models, and advanced machine learning approaches. The position is embedded
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CFD predictions with quantum gas LiDAR measurements, using simulated and experimental data to relate observed concentration fields to underlying emission sources. This will include the development
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expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Through-life Engineering Services (TES) Centre
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group as part of the diverse and international Nordcee section at the department, and will work closely together with associates from all RECLESS institutions. For more information about the position
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function of urban blue spaces influence perceptions. It will subsequently explore and evaluate the types of information and knowledge required to improve the understanding and appreciation of urban blue
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principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion phenomena and link speciation with spectroscopic signatures. Formal requirements include a Master's degree in
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for a research degree . In your EOI, nominate Dr Katharina Esau as your proposed principal supervisor, and copy the link to this scholarship website into question 2 of the financial details section
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, finance or computing science/natural language processing/machine learning. Preference will be given to those who are proficient in Python, adept at processing large-scale data and have worked with large
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a lack of a coherent and mathematically rigorous methodology for how health, environmental and population exposure and vulnerability data can be combined to optimally issue warnings in order to