Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of Nottingham
- University of East Anglia
- Imperial College London;
- University of Exeter;
- The University of Manchester
- Loughborough University
- Newcastle University
- The University of Manchester;
- University of Birmingham;
- University of Cambridge;
- University of Exeter
- AALTO UNIVERSITY
- Bangor University
- KINGS COLLEGE LONDON
- The University of Edinburgh;
- University of Birmingham
- University of Cambridge
- University of East Anglia;
- University of Sheffield
- University of Surrey
- University of Warwick
- ;
- Edinburgh Napier University;
- Loughborough University;
- Manchester Metropolitan University
- Oxford Brookes University
- Swansea University
- University of Bristol
- University of Nottingham;
- University of Oxford;
- University of Sheffield;
- European Magnetism Association EMA
- King's College London
- King's College London;
- Liverpool John Moores University
- Manchester Metropolitan University;
- Swansea University;
- The University of Edinburgh
- UCL
- Ulster University
- University of Bradford;
- University of Bristol;
- University of Essex
- University of Hull;
- University of Leeds
- University of Liverpool
- University of Liverpool;
- University of Oxford
- University of Plymouth
- University of Warwick;
- University of York;
- 42 more »
- « less
-
Field
-
-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms
-
Project Title: Intrinsically-aligned machine learning In a truly cross-disciplinary effort, this project, funded by the Leverhulme Trust and in collaboration with the University of Manchester, will leverage
-
expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
-
-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
-
that can be run. Emulating expensive processes could allow more data to be generated from better models, at lower cost. The central science question is: how can machine learning and evolutionary computation
-
PhD studentship in Computational Chemistry – Training force fields for computer-aided drug design with machine learning. Award Summary 100% fees covered, and a minimum tax-free annual living
-
PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027
-
PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
-
machine learning (TML). TML is a cross-disciplinary field that combines machine learning, security/privacy and transparency. As a doctoral researcher your goal is to conduct research in the fast-paced field
-
these barriers by putting together a world-leading data resource on suicide and self-harm, and powerful machine learning methodologies compatible with epidemiological principles to produce high-quality evidence