Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- University of Oxford;
- AALTO UNIVERSITY
- Durham University
- King's College London
- University of Liverpool
- Aston University
- Queen Mary University of London;
- UNIVERSITY OF VIENNA
- Imperial College London
- Nature Careers
- University of Bath
- Cardiff University
- Heriot Watt University
- King's College London;
- Plymouth University
- The University of Edinburgh;
- University of Cambridge;
- University of Canterbury, New Zealand;
- University of Lincoln
- University of Lincoln;
- University of Liverpool;
- University of York;
- 13 more »
- « less
-
Field
-
the drivers of extinction across space and time’. The post holder will provide guidance to less experienced members of the research group, including research assistants, technicians, and PhD and project
-
trustworthy machine learning, with a particular emphasis on mechanistic interpretability and its application to healthcare data. The successful candidate will contribute to understanding how modern machine
-
space and time’. The post holder will provide guidance to less experienced members of the research group, including research assistants, technicians, and PhD and project students. The post holder will
-
and machine learning to the selection of appropriate technologies. Disseminate findings through peer-reviewed publications, workshops, and conferences. Contribute to project management, reporting and
-
year-long module performance in the water industry; (ii) exploring whether machine learning, couple with transport informed models can be used to predict membrane fouling for specific applications
-
effects while building machine-learning-ready kinetic datasets for predictive catalyst design. You should have a PhD (or about to obtain) in Chemistry or field related to this project (Chemical Engineering
-
modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
-
developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
-
reports and grant proposals. You should possess a PhD or DPhil (or near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to jakob@robots.ox.ac.uk For more information about
-
“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of