Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oxford
- KINGS COLLEGE LONDON
- ;
- AALTO UNIVERSITY
- King's College London
- Heriot Watt University
- UNIVERSITY OF VIENNA
- University of London
- ; Technical University of Denmark
- Durham University
- Imperial College London
- King's College London;
- Medical Research Council
- Nature Careers
- Northumbria University;
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- Technical University of Denmark
- University of Cambridge
- University of Cambridge;
- University of Lincoln
- University of Manchester
- University of Reading
- University of West London
- 13 more »
- « less
-
Field
-
to reconstruct subsurface defects; Implement image/signal‑processing or machine‑learning pipelines for automated flaw characterisation; Collaborate with the Federal University of Rio de Janeiro, including short
-
intelligence experts to generate new projections of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet
-
of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
-
of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
-
observation-based climate datasets. In addition, we will also use innovative machine learning tools to evaluate the relationship between a set of hypothesised climatic precursor conditions, called (potential
-
will deliver projects that leverage large-scale electronic health record data and rich cytometry data derived from full blood count analysers to develop and refine machine learning models to improved
-
vision and machine learning methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection
-
, multimodal imaging, and AI-assisted diagnostics to enable safer and more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning
-
on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
-
experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models