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3.5-year D.Phil. studentship Project: Efficient engineering and control of predictable and reliable biotechnologies. Supervisors: Prof Antonis Papachristodoulou and Prof Harrison Steel This project
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is to discovering the mechanisms of resistance evolution and develop biomarkers that can predict which patients are at risk of developing resistance. Work at Manchester and Liverpool has focused
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
work well in real environments, achieving reliable results, correct use of tools, and predictable cost and speed across heterogeneous infrastructures and devices. To address this challenge, we
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functional synergies, you will design and test multi-strain microbial communities (SynComs) with consistent, predictable benefits for plants growing in complex soil environments. Aims and Objectives
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play a vital role in energy flow and carbon cycling in the ocean. Understanding how these communities are structured, and the factors that influence their organisation, is essential for predicting
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performance under environmental stress. Training in geospatial analysis and ecological modelling will enable predictive insight into urban ecosystem dynamics. You will develop expertise in stakeholder
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their own predictions (to support model-based decision-making and safety-critical applications) Candidate’s profile A good Bachelors degree (2.1 or above or international equivalent) and/or Masters degree in
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to increase each year. Tuition fees will also be paid. Home students are eligible. A funded PhD studentship is available in the field of computational inorganic chemistry. The project will involve prediction
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of barrier winds off East Greenland using new wintertime observations from a research cruise. Carry out numerical weather prediction simulations of barrier wind case studies with the observed sea-ice
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candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift), and tuition fees will be paid. Understanding and predicting fluid flow is essential