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computational while other work will involve time spent in the lab. Search for physics beyond the Standard Model in penguin decays in data from the LHCb experiment. Identify particle identification requirements
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trail experts, and computational scientists. It is highly likely completion of PhD will lead to post-Doctorate research opportunities on data science and statistical modelling as either an extension or a
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term
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are made where and when; supernovae (mechanisms and nucleosynthesis); gamma-ray bursts and their progenitors; modelling of Type I X-ray bursts and superbursts (thermonuclear explosions on the surface
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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courses Any HDR student willing to use mathematical modelling and simulation in engineering and physical problems. Eligibility criteria Strong mathematical background Strong analytic abilities Experience in
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the Collaborative Research Centre for Low Carbon Living. The project uses agent based modelling (ABM) to represent consumer behaviour, social networks and their responses to non-financial incentives and barriers in
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tools to record and derive important contextual information. The student will also learn relevant statistical techniques such as Linear Mixed Modelling to compare between drills and competition
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, spectroscopy, astrometry) using massive optical telescopes on Earth and in space (e.g., Hubble, Gaia, JWST, Kepler, TESS). My group develops cutting-edge models to extract the most from noisy data and to better