Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- Imperial College London
- Nature Careers
- University of Birmingham
- UNIVERSITY OF SOUTHAMPTON
- KINGS COLLEGE LONDON
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- The University of Southampton
- UNIVERSITY OF SURREY
- King's College London
- QUEENS UNIVERSITY BELFAST
- University of London
- University of Nottingham
- ; University of Oxford
- Brunel University
- CRANFIELD UNIVERSITY
- EMBL-EBI - European Bioinformatics Institute
- European University Institute
- Plymouth University
- Queen's University Belfast
- Queen's University Belfast;
- Technical University of Denmark
- The University of Edinburgh
- UCL;
- UNIVERSITY OF MELBOURNE
- University of Cambridge
- University of Glasgow
- University of Liverpool
- University of Manchester
- University of Oxford
- University of Plymouth;
- University of Sheffield
- University of Southampton;
- 23 more »
- « less
-
Field
-
in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
-
medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
-
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
-
Strong analytical skills and experience in developing and implementing machine learning/AI solutions using relevant languages and frameworks Excellent communication skills and proven ability to collaborate
-
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
-
annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
-
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
-
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
-
, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
-
, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and