Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- Imperial College London;
- University of Exeter;
- University of Nottingham
- AALTO UNIVERSITY
- Loughborough University
- The University of Manchester
- The University of Manchester;
- University of Cambridge;
- University of Sheffield
- ;
- Bangor University
- KINGS COLLEGE LONDON
- The University of Edinburgh;
- University of Birmingham
- University of Birmingham;
- University of Cambridge
- University of East Anglia;
- University of Warwick
- Edinburgh Napier University;
- Loughborough University;
- Oxford Brookes University
- University of Bristol
- University of Nottingham;
- University of Oxford;
- University of Sheffield;
- University of Surrey
- ; The University of Manchester
- ; University of Exeter
- European Magnetism Association EMA
- King's College London
- King's College London;
- Liverpool John Moores University
- Manchester Metropolitan University;
- Newcastle University
- The University of Edinburgh
- UCL
- Ulster University
- University of Bradford;
- University of Essex
- University of Exeter
- University of Leeds
- University of Liverpool
- University of Liverpool;
- University of Newcastle
- University of Oxford
- University of Plymouth
- University of Warwick;
- University of York;
- 40 more »
- « less
-
Field
-
with, cloud computing and virtualisation technologies Familiarity and hands-on experience with machine learning techniques desirable Desirable to have work experience (through internships or similar) in
-
sluggish diffusion kinetics of HEAs make them excellent candidates for resisting oxidation and corrosion in high-temperature steam. Guided by thermodynamic modelling and machine learning, we will identify
-
, Psychology, or a related field, to be awarded before March 1st, 2026. Essential skills include an ability to code (e.g., Python, R) and interpret data, knowledge of machine learning and statistics, and a
-
cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
-
framework that compares and blends complementary paradigms of physics informed machine learning (such as PINNs, ODIL)—to (i) super-resolve experimental data, (ii) infer unknown parameters such as the
-
ecosystem services such as carbon storage (1-4). Recent advances in satellite observations and machine learning provide novel opportunities to study extreme fires on a global scale. In a changing climate
-
designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
-
of the workflow. While the majority of the project is computer based, there is a small lab-based component in order to generate cell samples to be able to acquire the NMR data. Once proof of concept has been
-
avenues may include linking plankton size patterns to krill dynamics, carbon export or nutritional quality, or developing tools for rapid ecosystem monitoring using machine-learning approaches