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Field
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landscapes and emerging stressors. With opportunities for field or semi-field studies and close collaboration with leading academic and industry partners, this project is ideal for candidates who want
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multidisciplinary team working at the frontier of offshore and subsea engineering and renewable energy research supported by industry partners. They will develop advanced skills in experimental hydrodynamics
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
development. Opportunities to interact with other academic and industry partners working on advanced LLM systems. Access to computing resources for running LLM-based experiments. Who You Will Work with The lead
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and operators. You will gain expertise in cyber security, AI/ML, and human-centred design, working in state-of-the-art facilities and collaborating with industry and transport authorities. The project
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“sustainable,” emissions of PM2.5 are in fact highly dependent on the type of fuel and burning conditions. As emissions from traffic and industry decline, woodburning is expected to become an even larger
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mechanical accuracy for small organic molecule energetics, but are too slow for routine use in medicinal chemistry. This collaborative, industry-funded, computational project will i) use state-of-the-art
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Overview Qualification type: PhD Subject area: Microbiology and crop protection Location/Campus: Hatfield, College Lane Campus Start date: 1 Feb 2026 Closing application date: 10 Jan 2026 Duration
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and CASE partner involvement (SatSense Ltd) will provide high-quality technical mentorship and industry context and ensures both academic rigour and real-world relevance, strengthening the projects
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system will manage and adjust the way information is used in real time. Cranfield University is a globally recognised postgraduate institution focused on applied research and strong links with industry
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of mission design, manufacturing, operations, and disposal. This PhD project aims to advance sustainable space operations by developing a holistic lifecycle assessment framework and computational tool