Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- University of Nottingham
- Cranfield University
- University of Manchester
- ; University of Birmingham
- Harper Adams University
- ; Aston University
- ; Durham University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Swansea University
- ; The Open University
- ; The University of Edinburgh
- ; The University of Manchester
- ; UWE, Bristol
- ; University of Bradford
- ; University of Bristol
- ; University of East Anglia
- ; University of Exeter
- ; University of Oxford
- ; University of Reading
- ; University of Southampton
- ; University of Strathclyde
- ; University of Warwick
- Aston University
- Nature Careers
- University of Cambridge
- University of Newcastle
- University of Oxford
- University of Sheffield
- 19 more »
- « less
-
Field
-
to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
-
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
-
here: Demonstration of Deterministic QC with one clean qubit (DQC1) on a neutral atom array How to apply If you would like to apply then please read the guidance on applying for a PhD studentship here
-
and deterministic AI outputs is critical. This requires robust design principles and architectural changes to reduce variability and integrate smoothly with industrial control systems. Enhancing
-
. A non-deterministic AI machine learning model for the identical task would not offer this demonstrability or, critically, the repeatability of classical algorithm-based systems. Furthermore, there is
-
would co-develop the research objectives and select the methods to be implemented with supervisory support. Some ideas to discuss include integrating repeat GEDI LiDAR surveys with time-series
-
such as landslide movement style, runout, and how landslide hazards evolve over time. This Ph.D. project will leverage the analysis of new time-series data from cloud-based satellite image archives
-
practical importance to automotive industry. Classical electrochemical-thermomechanical (ECTM) models are typically deterministic and insufficient to identify most optimum battery materials within certain
-
nature-based solution opposed to traditional ‘grey’ engineering, offer catchment-level solutions by using natural processes to slow and store water through a series of diffused interventions. Historically
-
to air pollution in the future, and planning further policy changes. This PhD project will develop statistical modelling frameworks that are able to handle large-scale, complex, and correlated time series