Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- University of Nottingham
- ; Swansea University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Sheffield
- ; University of Southampton
- University of Sheffield
- ; City St George’s, University of London
- ; Cranfield University
- ; Lancaster University
- ; Newcastle University
- ; University of Exeter
- ; University of Nottingham
- ; University of Reading
- ; University of Warwick
- AALTO UNIVERSITY
- University of Cambridge
- ; Aston University
- ; Brunel University London
- ; Edge Hill University
- ; Loughborough University
- ; University of Cambridge
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Plymouth
- ; University of Strathclyde
- ; University of Surrey
- ; University of Sussex
- KINGS COLLEGE LONDON
- University of Manchester
- University of Newcastle
- 24 more »
- « less
-
Field
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
in a degree, ideally at Masters level, in an Engineering subject, Physics, Mathematics, Computer Science or other quantitative background. Knowledge in fluid mechanics, ocean waves, numerical methods
-
capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
-
alongside numerical simulations relying on high-performance computing and reduced order modelling. We aim to gain new insights about the physical coherent structures which are most relevant to viscoelastic
-
to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
-
, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
-
with a first class or upper second-class degree in engineering, physics, applied mathematics or a related field. A solid foundation in fluid dynamics and heat transfer, and experience with computer
-
-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
-
technical expertise in Computational Fluid Dynamics (CFD), simulation methods (including RANS, DNS/ LES), and experimental techniques such as wind tunnel testing and 3D printing. The project will also improve
-
refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain