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. This PhD will utilise thermodynamic modelling to obtain predictions of equilibrium and, where possible, non-equilibrium phases for a matrix of compositions covering flat rolled products including elevated
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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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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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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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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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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
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engineering wake models to mesoscale simulations with wind-farm parametrisation, when predicting wake effects and thus annual energy production. The student will improve current models used by industry such as
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, Nguyean et al. 2022). However, accurately predicting PB performance – particularly complex flow patterns within the structure and resulting inundation – requires advanced modelling techniques. This research
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datasets with phylogenies and environmental variables, the project aims to rapidly explore trait evolution, predict dispersal potential, and assess climate-related risks. This work bridges biodiversity