Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Nottingham
- UCL;
- UNIVERSITY OF SOUTHAMPTON
- Plymouth University
- KINGS COLLEGE LONDON
- Nature Careers
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Oxford
- Imperial College London
- Queen's University Belfast
- The University of Edinburgh;
- University of Bath;
- University of Birmingham
- University of Hertfordshire;
- University of Leeds
- University of Leeds;
- University of Southampton;
- ;
- CRANFIELD UNIVERSITY
- Cranfield University;
- EMBL-EBI - European Bioinformatics Institute
- Imperial College London;
- King's College London
- Middlesex University;
- Queen's University Belfast;
- The University of Southampton
- University of Exeter;
- University of London
- University of Manchester
- University of Nottingham;
- University of Plymouth;
- University of Stirling;
- University of Warwick;
- 23 more »
- « less
-
Field
-
, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning to project future data analysis. Managing and analysing large data sets using efficient data
-
Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
-
demand. Responsibilities Apply machine learning techniques, statistical modelling, and chemometric methods to extract meaningful biological insights from multivariate data and complex GCxGC-TOFMS datasets
-
to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
-
to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
-
data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and
-
on exploring what works and what doesn’t - in the care of people at the end of life. Learn more about the project here: NIHR Award Details. The study is led by Associate Professor Susie Pearce, who will also be
-
on exploring what works and what doesn’t - in the care of people at the end of life: Learn more about the project here: NIHR Award Details . The study is led by Associate Professor Susie Pearce, who will also be
-
policy relevance. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches
-
of subsurface processes. You will be responsible for leading the development of the approach, which could include transferring learning from other geographic regions and data types, machine learning methods