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will provide information of what criteria will be assessed at each stage of the recruitment process. Please note that this is a PhD level role but candidates who have submitted their thesis and are
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in London Diverse, inclusive, and collaborative work culture with staff networks and resources supporting your personal and professional wellbeing . Further information This is a Full time Fixed term
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large language models (LLMs) through retrieval-augmented generation (RAG). The successful candidate will work at the intersection of information retrieval, natural language processing, and machine
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. For more information about the project, studentship and application process please consult the advert: https://www.royalholloway.ac.uk/media/gc5f4vrp/rhul-british-library-collaborative-phd-hirsch-music
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health problems. Excellent organisational, interpersonal and communication skills, along with an interest in interdisciplinary research, are essential. Experience in computer programming is essential
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page after you click “Apply Now”. This document will provide information of what criteria will be assessed at each stage of the recruitment process. We pride ourselves on being inclusive and welcoming
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responses. You will also liaise with international collaborators, contribute to data analysis, and assist in publishing research findings. This is an exciting opportunity to be part of a dynamic research team
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student will generalise analytical methods in astrodynamics to develop a robust state estimation framework for cislunar trajectories. The student will harness data-driven methods to develop a new cislunar
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: 3.5 years Funding Full coverage of tuition fees and an annual maintenance tax-free stipend of £21,237 for both home and overseas students. Information on fee status can be found at https
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data-driven methodologies. The goal of the team is to identify the fundamental physical mechanisms which are at play in such turbulent cascades, and generate low-order models that describe them