Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of East Anglia
- Cranfield University
- University of Birmingham
- University of Cambridge;
- Imperial College London;
- Loughborough University
- The University of Manchester
- Edinburgh Napier University;
- University of Birmingham;
- University of East Anglia;
- University of Nottingham
- University of Sheffield
- KINGS COLLEGE LONDON
- Newcastle University
- The Institute of Cancer Research
- The University of Manchester;
- University of Newcastle
- University of Nottingham;
- University of Oxford
- University of Plymouth;
- ;
- ; Loughborough University
- ; The University of Edinburgh
- ; The University of Manchester
- ; University of Exeter
- AALTO UNIVERSITY
- Coventry University Group;
- Durham University;
- Edinburgh Napier University
- Imperial College London
- King's College London;
- Loughborough University;
- Newcastle University;
- The University of Edinburgh
- UCL
- UNIVERSITY OF VIENNA
- Ulster University
- University of Cambridge
- University of Exeter
- University of Exeter;
- University of Glasgow
- University of Greenwich;
- University of Leeds
- University of Liverpool
- University of Liverpool;
- University of Oxford;
- University of Plymouth
- University of Sheffield;
- 38 more »
- « less
-
Field
-
and loss of migration across western Europe. Benefitting from the strong expertise of the supervisory team in stork ecology, movement analysis and spatial models, the project will leverage large
-
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
-
intensity of these changes. This PhD project will ultimately enable aircraft to reroute safely and efficiently in real time as weather evolves. By merging scientific machine learning, large-scale data
-
wind farms in the UK and neighbouring countries is expected to triple in less than five years. Newer wind farms are also deploying very large turbines of 14 MW or more, meaning that wake effects between
-
. Yet, many stellar and planetary parameters remain systematically uncertain due to limitations in stellar modelling and data interpretation. This PhD project will develop Bayesian Hierarchical Models
-
river channels, altering their topography, destabilising banks, and changing how water and sediment move through large rivers. While these impacts are becoming clearer, what remains poorly understood is
-
threats to biodiversity. Freshwaters are disproportionately affected by such invasions, and home to a disproportionately large proportion of biodiversity, especially invertebrates. They also provide
-
narrow down what parts of our genome are actually important for defining modern human-specific biology. This project will analyse data from these ultra-large datasets, alongside data from our great apes
-
writing and implementing code alongside extracting information, trends, and patterns from large datasets. Topics to explore during this PhD project include: Investigating available software options Methods
-
. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large