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the model, its numerical implementation in a finite element code, and its validation against experimental data in collaboration with experimental collaborators. You will also engage with the hub activities
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practice of Open Science principles (e.g. data sharing, code sharing, public engagement and awareness). Self-motivated, proactive, and takes initiative to problem-solve Understanding of anxiety and
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of computer simulation, Large Language Models, or machine learning etc, but a working knowledge of data science research processes from a relevant quantitative discipline, and strong coding skills are essential
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genome assembly, gene and transposon annotation, phylogenomics. Candidates must demonstrate proficiency in coding and analyses through open-access repositories (github or zenodo). This project will expand
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code, and make effective use of computational resources. • To conduct individual and collaborative research projects under the direction of the Principal Investigator and with other
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that requires accurate sub-grid models (e.g., Particle-in-Cell or Vlasov codes) coupled to a hydrodynamic simulation. In general, charged-particle transport is a non-trivial task, not only because of the large
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experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools; Code synthesis
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- the English Case Study investigating SSVS pilot project courts. This includes data collection, including court observation and participants interviews, coding and analysis of data, the development of project
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characterisation systems, vacuum beamlines, high-pressure gas cells, and optical diagnostics. Our numerical simulations rely on our own open-source code, Luna.jl (https://github.com/LupoLab/Luna.jl ). More details
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code, and to evaluate their performance on various data sets and applications. To take part in regular meetings, both in-person and online with UK colleagues, and online with US colleagues, and to