Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oxford
- Durham University
- KINGS COLLEGE LONDON
- University of Reading
- DURHAM UNIVERSITY
- University of Oxford;
- ;
- Heriot Watt University
- City University London
- John Innes Centre
- King's College London
- King's College London;
- UNIVERSITY OF READING
- UNIVERSITY OF VIENNA
- University of Cambridge;
- University of Reading;
- AALTO UNIVERSITY
- Durham University;
- Imperial College London
- Medical Research Council
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- The University of Edinburgh;
- University of Bath
- University of Exeter;
- University of Glasgow
- University of Kent;
- University of Liverpool
- University of Sheffield
- University of York;
- 19 more »
- « less
-
Field
-
of the COLIBRE code This post is fixed term for 12 months from the start date. Successful applicants will, ideally, be in post before April 2026.
-
background in rheology and Non-Newtonian flows. In addition, they will be familiar with Machine-Learning tools (such as PyTorch or TensorFlow), as well as with code development and customisation. They should
-
Contribute to further developments of the COLIBRE code This post is fixed term for 12 months from the start date. Successful applicants will, ideally, be in post before April 2026. Where to apply Website https
-
The closing date for applications is 23.59 on 28th November 2025 Interview date: to be confirmed This post is for a fixed-term period of up to 18 months. By reference to the applicable SOC code
-
to completion of) a PhD/DPhil in geotechnical engineering, along with experience in numerical methods, including the implementation of soil constitutive models in finite element code. You should have excellent
-
, condition number theory, high-dimensional analysis (concentration of measure) and stochastic computation. A record of some high-quality mathematical reports and/or publications, as well as coding skills (e.g
-
Constrained experimental design Combining models and combining data / Realistic simulation of clinical trials Developing LLMs to utilise ODEs and ProbML as tools, Code synthesis for causality Generalisability
-
experimental design • Combining models and combining data / Realistic simulation of clinical trials • Developing LLMs to utilise ODEs and ProbML as tools, Code synthesis for causality
-
functional neuroimaging or omics information, and/or temporal data as recorded using smartphones and/or wearable devices Excellent statistical and coding skills with demonstrated ability to apply and combine
-
functional neuroimaging or omics information, and/or temporal data as recorded using smartphones and/or wearable devices Excellent statistical and coding skills with demonstrated ability to apply and combine