Sort by
Refine Your Search
-
Listed
-
Employer
- University of Oxford
- Nature Careers
- AALTO UNIVERSITY
- University of Cambridge;
- University of Liverpool
- University of Oxford;
- The University of Edinburgh;
- Durham University
- Heriot Watt University
- Imperial College London
- King's College London
- Queen Mary University of London;
- UNIVERSITY OF VIENNA
- University of Bath
- University of Dundee;
- University of Exeter
- University of Hull;
- University of Liverpool;
- University of York;
- 9 more »
- « less
-
Field
-
biological properties of LATs, including particle size, degradation rate, and drug release profiles. You will build representational methods for APIs and excipients, apply Bayesian optimisation to experimental
-
prompted by the same environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS
-
environmental stressors across a species’ geographic range and through time. The post holder will develop a new Bayesian model, MESS, to analyse the dynamics of extirpation. The MESS model will adapt
-
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
-
across Oxford, Nanyang Technological University and National University of Singapore studying the reliability of LLMs through the lens of uncertainty quantification (UQ), Bayesian inference, conformal
-
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
-
. The PDRA will quantify the differences in calculated and measured experimental conditions by adapting the Geodetic Bayesian Inversion Software ( https://doi.org/10.1029/2018GC007585) ). Working alongside our
-
) is developing ML methods supporting the experimental HT workflows and, so far, focused on intelligent design of experiments based on Bayesian Optimisation. The team at Cambridge has its own high
-
target validation to support TB drug discovery. The role will involve the application of liquid chromatography–mass spectrometry (LC-MS) approaches to quantify cellular metabolism and measure changes in
-
, including: i) Using multi-scale (within-host and population-scale) epidemiological models to optimise vaccine dosing (for vaccine development); ii) Improving vaccine targeting and timing for different