Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; The University of Manchester
- ; University of Birmingham
- ; University of Reading
- ; University of Warwick
- ; Loughborough University
- ; University of Leeds
- ; University of Oxford
- University of Cambridge
- ; Newcastle University
- ; University of Exeter
- ; University of Strathclyde
- University of Newcastle
- University of Oxford
- ; Aston University
- ; Brunel University London
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Swansea University
- ; The Open University
- ; University of East Anglia
- ; University of Nottingham
- ; University of Southampton
- ; University of Sussex
- Abertay University
- Heriot Watt University
- 17 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
-
enhance system reliability and safety, aligning with the UK’s NetZero targets. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess
-
. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
-
larger effort to map material performance limits and unlock untapped robustness in engineering alloys. You will: Develop and implement physics-based microstructural models to simulate damage and fatigue
-
of the particle fuel, crack initiation/propagation and failure mechanisms in relation to test temperature. Finite element (FE) modelling using FE tools such as Abaqus, (or) Ansys, (or) COMSOL is optional
-
by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
-
across both physical and digital technologies, to deliver a cyber-physical platform for the autonomous inspection, digital representation, and maintenance of highways. The successful candidate joins a
-
Supervisors: Dr Katherine Finlay, Psychology (Lead) Collaboration Partners: Dr Alexandra Oti, Unravel Health Dr Chanais Matthias, Manchester Metropolitan University Project Overview: Hormone-driven
-
an independent impact assessment of potential climate interventions in the Arctic marine environment through laboratory experiments and computer modelling. The team will develop physical, climate and ecosystem
-
modelling of laser shock peening. Molecular Dynamics (MD) and Finite Element (FE) simulations will be combined to account for the complex physical phenomena and their different scales. The interdependence