483 programming-language-"St"-"Washington-University-in-St"-"St" PhD positions in United Kingdom
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ranking university – in a relevant subject. EU and Overseas applicants should achieve an IELTS score of 6.5 with at least 6.0 in each competency. English language requirements: Applicants must meet the
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competency. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website . Funding information: Studentship type
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within the Institute of Health Informatics. The studentships will commence from Feb 2026. About the project Successful applicants will work with the programme coordinator to identify a project and a
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
Large language models (LLMs) can read and write text and code, call tools, and follow instructions. They now allow us to build agents that plan and act over many steps instead of giving a single
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to gain, at least a 2:1 degree (or equivalent) in a relevant degree programme, e.g., Geography, Environmental Science, Computer Science, Engineering, or an appropriate Master’s degree. English language
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in their native languages. This conversational AI will gather crucial, yet often unrecorded, data on symptoms, lifestyle, and social determinants of health, converting unstructured dialogue
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language model (LLM) technologies to create advanced, multimodal predictive tools for plant health monitoring. Using imagery from RGB cameras, drones, satellites, and multispectral and hyperspectral sensors
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science, engineering, mathematics, or related subject) Proficiency in English (both oral and written) Essential to have strong foundations in computer systems through degree courses or equivalent work experience
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(or equivalent) with a UK Masters degree (with average programme mark of no less than 65%) or International equivalent. English Language requirement of IELTS band 7.0 or above with not less than 7.0 in each
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and