Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- ; University of Warwick
- University of Nottingham
- ; University of Nottingham
- ; University of Surrey
- ; University of Birmingham
- ; University of Exeter
- ; University of Leeds
- ; University of Reading
- University of Cambridge
- ; Cranfield University
- ; Loughborough University
- ; The University of Edinburgh
- ; University of Oxford
- ; University of Sheffield
- ; University of Sussex
- University of Newcastle
- ; Manchester Metropolitan University
- ; Newcastle University
- ; UWE, Bristol
- ; University of Bristol
- ; University of East Anglia
- ; University of Southampton
- Imperial College London
- University of Sheffield
- ; Anglia Ruskin University
- ; Aston University
- ; City St George’s, University of London
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Midlands Graduate School Doctoral Training Partnership
- ; Queen Mary University of London
- ; Swansea University
- ; University of Copenhagen
- ; University of Greenwich
- ; University of Lincoln
- ; University of Plymouth
- ; University of Strathclyde
- Harper Adams University
- Liverpool John Moores University
- University of Birmingham
- University of Exeter
- University of Glasgow
- University of Liverpool
- University of Oxford
- 37 more »
- « less
-
Field
-
computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
-
. The research will combine computational modelling, experimental validation, and machine learning techniques to develop a predictive phenomenological PAC model. The successful applicant will develop and apply
-
development experience in the following areas: Machine Learning/AI, Internet of Things technologies. For further information, please contact Prof Gyu Myoung Lee G.M.Lee@ljmu.ac.uk . In return, we offer
-
, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
-
Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
-
& machine learning
-
Master’s degree in a relevant discipline (cognitive neuroscience, neuroscience, computational neuroscience, psychology, cognitive science, machine learning/data science/AI). Start date: 1 October 2025
-
through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
-
techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
-
(or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject area: Medical imaging, biomedical engineering, computer science & IT