Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of East Anglia
- University of East Anglia;
- Cranfield University
- University of Exeter
- KINGS COLLEGE LONDON
- Imperial College London;
- Newcastle University
- University of Birmingham
- University of Birmingham;
- University of Oxford
- Durham University;
- King's College London
- Newcastle University;
- University of Glasgow
- University of Manchester
- University of Newcastle
- University of Plymouth
- University of Warwick;
- ;
- ; University of Exeter
- ; University of Surrey
- AALTO UNIVERSITY
- Aston University;
- Bangor University
- Imperial College London
- Lancaster University
- Oxford Brookes University
- South West Biosciences Doctoral Training Partnership (SWBio DTP)
- Swansea University;
- The Medicines And Healthcare Products Regulatory Agency;
- The University of Manchester;
- UNIVERSITY OF VIENNA
- University of Bristol
- University of Cambridge
- University of Essex
- University of Glasgow;
- University of London
- University of Nottingham
- University of Nottingham;
- University of Plymouth;
- University of Reading;
- University of Salford;
- University of Sheffield;
- University of Sussex;
- University of Warwick
- 35 more »
- « less
-
Field
-
biostatistics. We develop, apply and promote innovative statistical and data science approaches to advance biomedical science and human health. The BSU current research portfolio is organised into five main
-
, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
-
spreads in natural waters. Training will cover microbial genomics, evolution assays, GIS, and advanced statistics, alongside transferable skills in interdisciplinary collaboration and science communication
-
, developing spatial statistical models, and translating results into actionable insights for policy and adaptation. The strength of the project lies in its interdisciplinarity, combining atmospheric science
-
patient samples. The Sheffield arm of the project will develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung
-
involving complex methodologies and statistical analysis. Your responsibilities will include data management, cleaning, and analysis using advanced statistical techniques, as well as drafting reports and
-
training dataset of well-studied volcanoes with known large eruptions, the project will employ statistical and machine learning (ML) methods to identify the strongest predictors of eruption magnitude
-
opportunity to devise an exciting research project, to receive training in data capture and manipulation, statistics, trait analysis, and modelling of interaction webs, and to undertake fieldwork
-
a Federated Learning approach to deploy the AI, ensuring robust privacy preservation of sensitive student data. The successful applicant will undertake advanced statistical analysis and stakeholder
-
flows, and to have developed skills in experimental fluid mechanics, statistics, data processing, machine learning, and mathematical modelling. Supervisors: Dr Kostas Steiros Duration: 3.5 years. Funding