Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- ;
- University of Newcastle
- ; Swansea University
- ; University of Surrey
- KINGS COLLEGE LONDON
- University of Cambridge
- University of Exeter
- University of Manchester
- University of Oxford
- University of Warwick
- ; Aston University
- ; Durham University
- ; Newcastle University
- ; University of Birmingham
- ; University of Bristol
- ; University of East London
- ; University of Greenwich
- ; University of Southampton
- ; University of Warwick
- AALTO UNIVERSITY
- Durham University
- Imperial College London
- King's College London;
- Loughborough University
- Loughborough University;
- The University of Manchester
- UNIVERSITY OF EAST LONDON
- UNIVERSITY OF VIENNA
- University of Glasgow
- University of Liverpool
- University of Nottingham
- University of Sheffield
- University of Surrey
- VIN UNIVERSITY
- 25 more »
- « less
-
Field
-
will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
-
is an AI-based technique that supports imitation of the preferred system behaviour by using its behavioural history. It helps in the inference of the reward values by taking the observed history
-
framework will be used with advanced causal inference methods – including inverse probability weighting to construct a valid comparison group. The analysis will use the potential outcomes approach to address
-
Bayesian inference framework for identifying complex aerospace systems combining with limited experimental data. It can be also used to quantify uncertainties from experimental testing, significantly
-
are suitable. The aims of this project are to Review operating characteristics proposed for rare disease trials Develop novel Bayesian operating characteristics for different types of rare disease trials Apply
-
for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
-
-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
-
supervisor Dr Lei Xing has built strong track record in circular chemical economy, green energy, carbon capture and utilisation, AI and digitalisation. Supervisors:Dr Lei Xing and Professor Jin Xuan Entry
-
in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
-
practices (e.g., Buddhism, you will have a track record of interdisciplinary work at the nexus of artificial intelligence and neuroscience. Experience collecting multi-modal neurophysiological data would be