Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- University of East Anglia
- University of Nottingham
- AALTO UNIVERSITY
- University of Sheffield
- ;
- Bangor University
- Loughborough University
- The University of Manchester
- The University of Manchester;
- University of Cambridge
- University of Cambridge;
- ; The University of Manchester
- KINGS COLLEGE LONDON
- Oxford Brookes University
- University of Birmingham
- University of Bristol
- University of East Anglia;
- University of Nottingham;
- University of Sheffield;
- University of Surrey
- University of Warwick
- ; City St George’s, University of London
- ; Coventry University Group
- ; University of Exeter
- ; University of Nottingham
- Abertay University
- Harper Adams University
- King's College London;
- Liverpool John Moores University
- Loughborough University;
- Manchester Metropolitan University;
- Nature Careers
- Newcastle University
- The University of Edinburgh
- The University of Edinburgh;
- UCL
- University of Birmingham;
- University of Exeter
- University of Leeds
- University of Newcastle
- University of Oxford
- 32 more »
- « less
-
Field
-
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
-
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
-
sequencing and researching disease in patient cohorts, working with machine learning techniques and programming computers. The candidate will learn about different flavors of metagenomic sequencing, how
-
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
-
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
-
for complex data accessible to the scientific community and to produce innovative methodology related to trial designs, longitudinal and event history data, precision medicine, causal inference, AI/machine
-
built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
-
by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
-
data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
-
. 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