Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Nottingham
- University of Warwick;
- The University of Manchester
- University of Birmingham
- University of Exeter
- Manchester Metropolitan University
- University of Warwick
- AALTO UNIVERSITY
- Durham University
- Newcastle University
- UNIVERSITY OF VIENNA
- University of Bedfordshire
- University of Cambridge
- University of Cambridge;
- University of Newcastle
- University of Oxford
- University of Sheffield
- City St George’s, University of London
- Harper Adams University
- King's College London;
- LIVERPOOL JOHN MOORES UNIVERSITY
- Manchester Metropolitan University;
- Oxford Brookes University
- The Rosalind Franklin Institute
- University of Birmingham;
- University of Bristol
- University of Plymouth
- University of Salford
- University of Sheffield;
- University of Sussex
- 20 more »
- « less
-
Field
-
About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
-
filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application
-
4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
We welcome applications from candidates with a broad range of academic backgrounds and experiences for a 4-year PhD project on Mathematical and Machine Learning Aspects in Crystallography at Durham
-
. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role. Candidate requirements Candidates must have expertise in developing computer vision and machine learning
-
discipline. Essential Good programming skills, preferably in Python/C#. Experience with machine learning, deep learning, or experimental AI evaluation. Interest in secure distributed AI, federated learning
-
and advanced machine learning. The project will integrate measurements from the SWOT satellite mission with Oxford's Global River Topology (GRIT) hydrography to develop verified, uncertainty-aware
-
discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
-
automotive and aerospace electrification. Applications for this PhD position are invited at the Power Electronics and Machines Centre, University of Nottingham. Based in a recently built £18M facility
-
integrating power electronic converters and electrical machines we can use common structures and systems to greatly reduce, material usage and energy consumption. Through a multidisciplinary research approach
-
This PhD asks a different question: instead of demanding more data, can we build language models that learn smarter from less? You will design AI architectures that adapt to the structure of a