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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of next generation agentic AI systems. In this PhD programme, you will redefine how the world works, learns, and discovers, turning bold ideas into tools used by millions. You will then become one
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lost productivity. The popularity of domestic wood stoves is increasing rapidly, with an estimated 150,000–200,000 units sold in 2022 alone. While manufacturers often market these stoves as “clean” and
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monitoring through considering a wider area of the floodplain. Repeated detailed topographical surveys using a differential Global Positioning System (dGPS) will be used to quantify changes in geomorphology
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and industrial partners, you will deliver models and tools that enhance the stability and reliability of future low-inertia power systems. What we are looking for You must have a PhD in Electrical
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deliver models and tools that enhance the stability and reliability of future low-inertia power systems. You must have a PhD in Electrical Engineering, Control Engineering, Physics or Mathematics
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Application Deadline: Applications accepted all year round How to apply: https://uom.link/pgr-apply-2526 UK Only This 3.5-year PhD studentship is open to Home (UK) applicants. The successful
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Profile First Stage Researcher (R1) Positions PhD Positions Country United Kingdom Application Deadline 7 Dec 2025 - 17:00 (Europe/London) Type of Contract Temporary Job Status Full-time Is the job funded
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will
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A PhD position is available at the Theory and Foundations group in the Department of Computer Science, University of Warwick, UK. The group works on various aspects of theoretical computer science
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). The Computer Science group is looking for students to work on one of the following projects Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures Carbon-Conscious Resource Scheduling for AI Workloads