Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Nottingham
- ; The University of Manchester
- ; University of Nottingham
- ; University of Exeter
- ; University of Warwick
- ; University of Leeds
- ; Newcastle University
- ; Swansea University
- ; University of Oxford
- ; University of Surrey
- ; Cranfield University
- ; The University of Edinburgh
- ; University of Birmingham
- ; University of Bristol
- ; University of Reading
- ; University of Southampton
- Abertay University
- Imperial College London
- University of Cambridge
- ; Aston University
- ; Brunel University London
- ; City St George’s, University of London
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Queen Mary University of London
- ; UCL
- ; UWE, Bristol
- ; University of East Anglia
- ; University of Greenwich
- ; University of Strathclyde
- ; University of Sussex
- Harper Adams University
- Newcastle University
- University of Liverpool
- University of Newcastle
- University of Sheffield
- 29 more »
- « less
-
Field
-
Machine Learning-based diagnostics and prognostics digital twin system will be developed, aiming to provide fast and reliable predictions of the health of gas turbine engines. Non-confidential operational
-
one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
-
into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
-
modelling and simulation techniques and software packages would be an advantage. Programming skills in languages such as Python, C++, MATLAB, are desirable, as is an awareness of machine learning or other AI
-
into hydrogen and nitrogen under practical onboard conditions. Successful candidate will develop and apply computational methods, such as density functional theory based atomistic modelling and machine learning
-
approaches (e.g. SPG) as well as the use of machine learning, advanced computing, statistical modelling to explore the stochastic response to complex scenarios. This project offers the opportunity to undertake
-
calculations of well-characterized 2D materials, simulations of electron microscopy images, and machine learning methods to reconstruct the 3D atomic positions of materials from a 2D microscopy image. The
-
this overall aim, the student will Employ computer programming methods to determine the occurrence of Alzheimer disease in obstructive sleep apnoea patients using previously collected clinical data and Perform
-
powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
-
and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group