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programs. The student will join a well-supported team of chemists and biochemists (Master’s and PhD students, and postdocs) who are well-placed to provide a supportive and ambitious peer group. The student
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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Thrive Global and two international partners in the United States and Sweden have been funded to deliver a new research programme, titled Collective Action for Race Equity in Health and Social Care (CARE
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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Thrive Global and two international partners in the United States and Sweden have been funded to deliver a new research programme, titled Collective Action for Race Equity in Health and Social Care (CARE
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the Principal Investigator and University of Cambridge Postdoc Academy. Fixed-term: The funds for this post are available for 3 years in the first instance, based on an anticipated start date of 01 April 2026
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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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methods and interdisciplinary approaches and has dedicated awards to offer in these areas. For more information on the LISS DTP core principles and vision please go to our webpage here . Research in
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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