Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; Swansea University
- ; The University of Manchester
- ; University of Birmingham
- ; University of Southampton
- ; University of Surrey
- University of Cambridge
- University of Sheffield
- ; Newcastle University
- ; The University of Edinburgh
- ; City St George’s, University of London
- ; Cranfield University
- ; University of Exeter
- UNIVERSITY OF VIENNA
- University of Newcastle
- ; Loughborough University
- ; University of Nottingham
- Harper Adams University
- University of Oxford
- ; University of Bristol
- ; University of Oxford
- ; University of Warwick
- AALTO UNIVERSITY
- Imperial College London
- The University of Manchester
- ; Brunel University London
- ; University of Cambridge
- ; University of East Anglia
- ; University of Reading
- ; University of Sheffield
- Abertay University
- KINGS COLLEGE LONDON
- Loughborough University;
- THE HONG KONG POLYTECHNIC UNIVERSITY
- The University of Edinburgh;
- University of Bristol
- University of Sheffield;
- ; Aston University
- ; Coventry University Group
- ; Durham University
- ; Imperial College London
- ; King's College London
- ; Manchester Metropolitan University
- ; St George's, University of London
- ; University of Greenwich
- ; University of Huddersfield
- ; University of Leeds
- ; University of Plymouth
- ; University of Strathclyde
- ; University of Sussex
- Coventry University Group;
- King's College London;
- Manchester Metropolitan University
- Newcastle University
- The University of Manchester;
- UCL
- University of Birmingham
- University of Bristol;
- 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 Strathclyde;
- University of Warwick
- University of Warwick;
- 61 more »
- « less
-
Field
-
challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
-
mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
-
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
-
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
-
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
-
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