Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- ;
- Cranfield University
- University of Cambridge
- ; The University of Manchester
- University of Nottingham
- ; University of Southampton
- ; Newcastle University
- ; University of Birmingham
- ; University of Nottingham
- ; University of Surrey
- ; Swansea University
- ; University of Oxford
- Harper Adams University
- Imperial College London
- University of Newcastle
- ; City St George’s, University of London
- ; University of Cambridge
- ; University of Exeter
- ; University of Warwick
- AALTO UNIVERSITY
- Abertay University
- ; Aston University
- ; Cranfield University
- ; EPSRC Centre for Doctoral Training in Green Industrial Futures
- ; Loughborough University
- ; Manchester Metropolitan University
- ; St George's, University of London
- ; The University of Edinburgh
- ; University of East Anglia
- ; University of Reading
- ; University of Sussex
- University of Liverpool
- University of Oxford
- University of Sheffield
- 24 more »
- « less
-
Field
-
for extracting physiological biomarkers from ECG, PPG, and related sensor data Machine learning and AI for predictive modelling and risk stratification Computational physiology modelling to personalise and
-
The overall aim of this PhD project is to understand how indoor air quality (IAQ) affects the health of children and adolescents, including their mental health with the aim of creating healthier
-
motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
-
with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
-
Qualification Type: PhD Location: Nottingham Funding For: UK Students Funding amount: Full tuition fee waiver pa (Home Students only) and stipend at above UKRI rates pa (currently at £20,780
-
needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
-
experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
-
models. PIML can learn from small amounts of data and are more immune to hallucinations than conventional AI, making them exceptionally suited for biomedical applications. Research Environment You will
-
integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
-
experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter