Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- ;
- ; University of Southampton
- University of Nottingham
- ; University of Sheffield
- Cranfield University
- ; Newcastle University
- ; University of Leeds
- ; The University of Manchester
- ; University of East Anglia
- ; The University of Edinburgh
- ; University of Warwick
- Swansea University
- University of Exeter
- ; Swansea University
- ; University of Nottingham
- ; City, University of London
- ; Loughborough University
- ; University of Exeter
- ; University of Surrey
- Newcastle University
- University of Sheffield
- ; Cranfield University
- ; Imperial College London
- ; Northeastern University London
- ; UCL
- ; University of Cambridge
- ; University of Greenwich
- ; University of Hertfordshire
- ; University of Plymouth
- University of Cambridge
- ; Aston University
- ; Babraham Institute
- ; Leeds Beckett University
- ; Midlands Graduate School Doctoral Training Partnership
- ; UWE, Bristol
- ; Universitat Bern
- ; University of Birmingham
- ; University of Bristol
- ; University of Dundee
- ; University of Essex
- ; University of Oxford
- ; University of Sussex
- ; Western University
- Abertay University
- Harper Adams University
- The University of Manchester
- Ulster University
- University of Aberdeen
- University of Hertfordshire
- University of Oxford
- 40 more »
- « less
-
Field
-
Machine Learning. Candidates must have an MSc at distinction level (or equivalent) in applied mathematics, numerical analysis or similar and be eligible for UK fee status. Candidates should have a solid
-
Funding providers: Swansea University's Faculty of Science and Engineering Subject areas: Computer Science (Machine Learning/Pattern Recognition applied to molecular cardiology) Project start date
-
screening and process optimisation platforms has the potential to significantly streamline this process through machine learning guided experimentation. This interdisciplinary project, based across
-
Uncertainty quantification for machine learning models of chemical reactivity In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions
-
View All Vacancies Chemistry Location: UK Other Closing Date: Sunday 12 May 2024 Reference: SCI266 Uncertainty quantification for machine learning models of chemical reactivity In this PhD
-
Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi Project description: The University of Southampton
-
challenge hindering further progression is the lack of a high-throughput, automated, unbiased approach to analysing and characterising hydrides in 2D and 3D. The use of deep learning (DL) based algorithms
-
. Computer programming in R, Python or STATA English Language requirements: IELTS 6.5 overall (minimum of 5.5 in all other sub-skills) Desirable: Masters in AI or Machine learning would be advantageous How
-
Cardiomyocyte Dynamic Network Analysis with Machine Learning (Ecidna-ML) This project represents a new approach to map dynamical interactions in networks of human cardiac cells. Network dyssynchronisation is a
-
expertise in machine learning and image processing, heritage scientists, conservators and curators. Summary of Project: In the cultural heritage sector, there is a long tradition of using complementary