Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ;
- University of Nottingham
- ; The University of Manchester
- ; Swansea University
- ; University of Nottingham
- University of Cambridge
- University of Sheffield
- ; University of Southampton
- ; University of Birmingham
- ; University of Surrey
- AALTO UNIVERSITY
- ; Brunel University London
- ; Cranfield University
- ; University of Bristol
- ; Loughborough University
- ; Newcastle University
- ; University of Cambridge
- ; Aston University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Imperial College London
- ; King's College London
- ; The University of Edinburgh
- ; University of Exeter
- ; University of Hertfordshire
- ; University of Oxford
- ; University of Plymouth
- ; University of Reading
- ; University of Sheffield
- ; University of Strathclyde
- ; University of Sussex
- Aston University
- Harper Adams University
- Heriot Watt University
- Imperial College London
- Newcastle University
- University of Liverpool
- University of Manchester
- 28 more »
- « less
-
Field
-
: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
-
areas, and be able to creatively combine disciplines to make new research advances in fluid mechanics. You will be creating data-driven algorithms which can solve state estimation problems in fluid
-
computational mechanics or multiphysics modeling, with particular interest in fracture mechanics and chemo-mechanical degradation. Knowledge of solid-state defect chemistry (advantageous). You will join a dynamic
-
mechanisms, with the current generation having significant drawbacks, including low energy efficiency, high operating voltage or temperature. This project will develop the materials, methods, and designs
-
computational modelling to be used to design and re-engineer flower architecture. The RA's main focus will be on computational modelling of gene regulatory networks for predicting the mechanisms leading
-
filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
-
synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application to the analysis of time series. In particular, the project
-
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
-
addressing the behaviour of thin foil materials for aerospace forming applications. The successful candidate will have a first-class or upper second-class honours degree in mechanical engineering or a related
-
at Nottingham https://www.nottingham.ac.uk/coatings/ is an international reference for all Thermal Barrier Coating activities. This PhD programme, in partnership with Rolls-Royce, will address key challenges