Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Cambridge
- University of Nottingham
- ;
- University of Sheffield
- ; Swansea University
- ; University of Birmingham
- AALTO UNIVERSITY
- Imperial College London
- ; The University of Manchester
- KINGS COLLEGE LONDON
- The University of Manchester
- UNIVERSITY OF VIENNA
- ; City St George’s, University of London
- ; The University of Edinburgh
- ; University of Bristol
- ; University of Cambridge
- ; University of Southampton
- ; University of Surrey
- Nature Careers
- Newcastle University
- The University of Manchester;
- University of Newcastle
- ; Brunel University London
- ; Coventry University Group
- ; Cranfield University
- ; Manchester Metropolitan University
- ; Newcastle University
- ; University of Huddersfield
- ; University of Oxford
- ; University of Plymouth
- ; University of Strathclyde
- ; University of Sussex
- Abertay University
- Brunel University London
- Kingston University
- Oxford Brookes University
- UNIVERSITY OF SOUTHAMPTON
- University of Bristol
- University of East Anglia
- University of Exeter
- University of Exeter;
- University of Greenwich
- University of Hertfordshire
- University of Liverpool
- University of Manchester
- University of Nottingham;
- University of Oxford
- University of Surrey
- VIN UNIVERSITY
- 40 more »
- « less
-
Field
-
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
-
, outperforming conventional static, dynamic and AI-based techniques. Specifically, in case of any inconsistency, the tool produces a counter example with values that constitute the vulnerability. Such pressure
-
dynamic School of Biological Sciences (https://www.southampton.ac.uk/about/faculties-schools-departments/school-of-biological-sciences ). The project will involve close collaboration with an international
-
with intelligent technologies. These agents will enable the creation of dynamic, evolving services across various sectors, including healthcare, urban intelligence, and education, fostering continuous
-
season properties (e.g. number, intensity) for lead times ranging from one to approximately six months in the latest generation of dynamical seasonal and decadal forecast models. Seasonal forecasts
-
, ensuring stable operation even as system dynamics evolve. Recent advances in Modular Multilevel Converter (MMC) topologies, along with developments in battery and supercapacitor technologies, create new
-
to arrange the tuition fees and living expenses. Find out more about fees here . Cranfield Doctoral Network Research students at Cranfield benefit from being part of a dynamic, focused and professional study
-
capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
-
-sensory perceptions, design features and vehicle dynamics. A focus on these factors not only has the potential to improve customer satisfaction but also reduce driver workload, increase usability and
-
factors influence this, including multi-sensory perceptions, design features and vehicle dynamics. A focus on these factors not only has the potential to improve customer satisfaction but also reduce driver