Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; Swansea University
- ; The University of Manchester
- ; University of Birmingham
- University of Sheffield
- ; University of Southampton
- University of Cambridge
- ; Newcastle University
- ; The University of Edinburgh
- ; University of Exeter
- ; University of Surrey
- UNIVERSITY OF VIENNA
- ; City St George’s, University of London
- ; Cranfield University
- ; Loughborough University
- ; University of Bristol
- ; University of Nottingham
- ; University of Sheffield
- ; University of Warwick
- AALTO UNIVERSITY
- Harper Adams University
- Imperial College London
- The University of Manchester
- University of Newcastle
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; University of Oxford
- University of Oxford
- ; Brunel University London
- ; University of Cambridge
- ; University of East Anglia
- ; University of Leeds
- ; University of Reading
- ; University of Strathclyde
- Abertay University
- KINGS COLLEGE LONDON
- Newcastle University
- THE HONG KONG POLYTECHNIC UNIVERSITY
- University of Birmingham
- University of Bristol
- ; Aston University
- ; Coventry University Group
- ; Durham University
- ; Imperial College London
- ; King's College London
- ; Manchester Metropolitan University
- ; Royal Northern College of Music
- ; St George's, University of London
- ; University of Greenwich
- ; University of Huddersfield
- ; University of Plymouth
- ; University of Sussex
- Aston University
- King's College London
- Loughborough University
- Manchester Metropolitan University
- UCL
- University of Cambridge;
- University of Exeter
- University of Glasgow
- University of Greenwich
- University of Liverpool
- University of London
- University of Nottingham;
- University of Plymouth
- University of Warwick
- 57 more »
- « less
-
Field
-
needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
-
Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
-
Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
-
Research theme: Fluid Mechanics, Machine Learning, Ocean Waves, Ocean Environment, Renewable Energy, Nonlinear Systems How to apply: How many positions: 1 Funding will cover UK tuition fees and tax
-
Large Language Models (LLMs) are reshaping how we interact with language technologies, yet many questions remain about what these models actually “know” about language. The University of Exeter is
-
address Dr Chunwei Xia: c.xia@leeds.ac.uk Co-supervisor’s full name & email address Professor Zheng Wang: z.wang5@leeds.ac.uk Project summary Large Language Models (LLMs) have profoundly transformed the way
-
corrosion-fatigue conditions by integrating multiscale physics-based models combined with mesoscale experimental tests. This research will study the effects of corrosion-induced changes in composition
-
, a state-of-the-art process-based model for groundwater risk assessment and contaminant transport modeling. By improving predictive modeling of transient contaminant source terms, this research will
-
challenges in the area of hazard assessment and impact forecasting. The aim of the project is to develop methodologies for forecasting future energy use for various assets and weather scenarios from short term
-
continuous lifetime treatment. Recent efforts to cure HIV infection have focused on developing latency reversing agents as a first step to eradicate the latent reservoir and animal models are being studied