Sort by
Refine Your Search
-
Listed
-
Employer
- Cranfield University
- ;
- ; Swansea University
- University of Cambridge
- ; Edge Hill University
- ; The University of Manchester
- ; University of Surrey
- ; University of Warwick
- 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
- King's College London
- UNIVERSITY OF EAST LONDON
- UNIVERSITY OF VIENNA
- University of Exeter
- University of Nottingham
- 12 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
-
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
-
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
-
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
-
utilise numerical techniques including the finite element method to describe biofluid flow and deformation in the human brain tissue. Parameters are inferred from clinical data including medical images
-
. This project aims to dive into the dynamics of attack methodologies (e.g., Membership Inference, Property Inference) and defensive mechanisms (e.g., Differential Privacy, Machine Unlearning) within FL
-
improve surgical workflow, shortens surgery time, enables unrestricted movement tracking, and reduces infection risks. Eliminating markers enables robot-assisted or fully automated femoral implantation