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Model Based Design and Flight Testing of a Vertical Take-Off Vertical Landing Rocket (C3.5-MAC-John)
tested will have applications for landing on other planets or moons, or even propulsive landing of rocket stages on Earth. These missions require the use of novel guidance algorithms, sensors, and control
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liquid argon. The analysis of the ProtoDUNE data will help to validate calibration techniques and particle identification algorithms. The candidate should have a good knowledge of particle physics and
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also be involved in improving object reconstruction, such as developing advanced algorithms for tau lepton identification or jet substructure / particle-flow techniques to identify Higgs bosons decaying
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processes that could be realised in neuromorphic hardware. The research will combine theoretical derivation and simulation-based validation, using mathematical modelling, algorithmic experimentation, and
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in reviewing, testing, refining, and providing feedback on historical records that are automatically transcribed, coded, and linked using computer algorithms. Support the project and technical team in
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deep learning algorithms in ESRI ArcGIS or similar software. Desirable Application Proficiency with relevant specialised software and approaches (e.g., geographic information systems, high-performance
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use world-leading instrumentation and methods developed here in Sheffield, supported by other students, postdocs and technical staff. Your development as a rounded, multidisciplinary researcher is a key
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of bacteria to membrane-targeting antibacterial compounds. This postdoc position will be responsible for the biophysical exploration using various ultrafast spectroscopy techniques, primarily time-resolved
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. This PhD project addresses that challenge by designing and optimising a dual-purpose battery energy storage system (BESS) with two complementary functions: (1) storing and distributing energy for stationary
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distributed tasks such as inspection of a big geographic area for fires is an important objective of this project. The project aims are to: 1) provide methodological contributions towards intelligent sensing