Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- University of Birmingham
- Imperial College London
- KINGS COLLEGE LONDON
- King's College London
- UNIVERSITY OF SOUTHAMPTON
- Nature Careers
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Queen's University Belfast
- University of Nottingham
- CRANFIELD UNIVERSITY
- QUEENS UNIVERSITY BELFAST
- The University of Southampton
- University of Cambridge
- University of Oxford
- ; Maastricht University
- ; University of Exeter
- ; University of Oxford
- Birmingham City University
- Cranfield University
- Manchester Metropolitan University
- UNIVERSITY OF MELBOURNE
- UNIVERSITY OF SURREY
- University of Glasgow
- University of Leeds
- University of Lincoln
- University of Liverpool
- University of London
- University of Manchester
- University of Sheffield
- University of Stirling
- University of Surrey
- 22 more »
- « less
-
Field
-
engineering science, with knowledge and/or some experience of energy technology and policy; and/or quantitative analysis including econometrics, statistics and machine learning and related disciplines handling
-
conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
-
. The researcher would be expected to have knowledge of protein structure, protein ligand binding, machine learning and expertise in workflow development. Information generated by the project will be widely
-
to) fundamental research in machine learning or statistics, algorithm design, the application of AI methods in science, healthcare, social sciences, or business. You should have a PhD or equivalent level of
-
are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data
-
using hybrid models combining mechanistic, GenAI, and machine learning approaches. You’ll contribute to building disease-specific Digital Twins using large-scale single-cell multi-omics datasets
-
10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
-
time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
-
. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
-
themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML