Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- University of Sheffield
- ; The University of Manchester
- ; Swansea University
- ; University of Bristol
- ; University of Warwick
- ; University of Sheffield
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Exeter
- ; University of Sussex
- ; City St George’s, University of London
- ; University of Nottingham
- ; University of Oxford
- ; University of Southampton
- University of Cambridge
- University of Newcastle
- ; Aston University
- ; Coventry University Group
- ; Cranfield University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Newcastle University
- ; University of Cambridge
- ; University of East Anglia
- ; University of Reading
- ; University of Surrey
- AALTO UNIVERSITY
- Harper Adams University
- Imperial College London
- 22 more »
- « less
-
Field
-
electron microscopy image simulations Development of a machine learning model capable of inferring 3D atomic structure from two-dimensional TEM projection images Application of the new approach
-
, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
-
to a wide range of materials, making impactful tools for the scientific community. The models and predictions in the project will be tested against real experimental data and used to drive the design of
-
University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics
-
aircraft. While working on this exciting research project, you will be provided with: A fully funded 4 year full-time PhD - £24,000 tax-free stipend per year. Attendance/presentations to international and
-
The research in this doctoral opportunity will develop a failure model that can represent the combined effect of surface and bending failures in gears to perform reliable health prognostics. Lack
-
unbounded variable and instance sets. In addition, novel approaches such as Physics Informed/Guided Learning allows the learning models to capture the underlying physics/patterns and to generate physically
-
Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof
-
of big data might not be possible to be captured by traditional modelling approaches. This implies that mathematical modelling of such data is infeasible. The data-driven modelling approach could resolve
-
Modelling post combustion amine CO2 capture plant School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin Hughes