Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- Imperial College London;
- University of Nottingham
- The University of Manchester
- University of Cambridge;
- University of Exeter;
- Loughborough University
- Newcastle University
- University of Birmingham;
- University of Exeter
- AALTO UNIVERSITY
- Bangor University
- KINGS COLLEGE LONDON
- The University of Edinburgh;
- The University of Manchester;
- University of Birmingham
- University of Cambridge
- University of East Anglia;
- University of Sheffield
- University of Surrey
- University of Warwick
- ;
- Edinburgh Napier University;
- Loughborough University;
- Manchester Metropolitan University
- Oxford Brookes University
- Swansea University
- University of Bristol
- University of Nottingham;
- University of Oxford;
- University of Sheffield;
- European Magnetism Association EMA
- King's College London
- King's College London;
- Liverpool John Moores University
- Manchester Metropolitan University;
- Swansea University;
- The University of Edinburgh
- UCL
- Ulster University
- University of Bradford;
- University of Essex
- University of Hull;
- University of Leeds
- University of Liverpool
- University of Liverpool;
- University of Newcastle
- University of Oxford
- University of Plymouth
- University of Warwick;
- University of York;
- 42 more »
- « less
-
Field
-
personalised, ethnically-stratified risk scores. This is a highly interdisciplinary project at the intersection of machine learning, health equity, and precision medicine. The successful candidate will join a
-
Environment - Wiley Online Library Additive Manufacturing: A Comprehensive Review Big data, machine learning, and digital twin assisted additive manufacturing: A review - ScienceDirect Full article: Achieving
-
, neuroscience, machine learning, or related fields and/or merit/distinction-level performance in a relevant postgraduate degree (e.g. MSc) Experience of working in a neuroscience, clinical or engineering research
-
properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
-
properties of representative sediment classes. · Evaluate methods for predicting sediment type and physical properties from geophysical data using machine learning. · Assess the reliability
-
and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
-
accelerators originally designed for artificial intelligence. These accelerators achieve exceptional performance by using low precision arithmetic, which is sufficient for machine learning tasks but much too
-
filled. This fully funded PhD explores AI-native and sensing-aware wireless systems where communications and sensing are co-designed end-to-end. You will unify modern machine learning, statistical signal
-
? This PhD project offers a unique opportunity to apply machine learning to solve a critical engineering challenge within the railway industry. The Challenge: Rail grinding is a crucial maintenance activity
-
. You will focus on machine learning, but will be involved in all areas. There are also spinout opportunities. For details: PhD information sheet The team have wide experience studying bumblebee behaviour