Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- Cranfield University
- ; University of Warwick
- ; University of Reading
- University of Cambridge
- ; Durham University
- ; Swansea University
- ; University of Exeter
- ; University of Surrey
- ; Edge Hill University
- ; Loughborough University
- ; University of Birmingham
- ; University of Bristol
- ; University of Sheffield
- Abertay University
- University of Nottingham
- ; Brunel University London
- ; Cranfield University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Midlands Graduate School Doctoral Training Partnership
- ; Newcastle University
- ; The University of Manchester
- ; Trinity College Bristol
- ; University of Copenhagen
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Nottingham
- ; University of Oxford
- ; University of Southampton
- ; University of Sussex
- 20 more »
- « less
-
Field
-
of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
-
addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods. The student will be
-
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
-
; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 2 months ago
ESRC DTP Strategic Joint Studentship University of Nottingham and University of Birmingham The Midlands Graduate School is an accredited Economic and Social Research Council (ESRC) Doctoral Training Partnership (DTP). One of 15 such partnerships in the UK, the Midlands Graduate School is a...
-
techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
-
optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
-
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
-
-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
-
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
-
for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters