Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oxford
- University of Oxford;
- AALTO UNIVERSITY
- Durham University
- King's College London
- Queen Mary University of London;
- UNIVERSITY OF VIENNA
- ;
- KINGS COLLEGE LONDON
- Nature Careers
- Plymouth University
- University of Bath
- University of Cambridge;
- University of Liverpool
- University of Liverpool;
- University of London
- Aston University
- Cardiff University
- Durham University;
- Heriot-Watt University;
- Imperial College London
- King's College London;
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- The University of Edinburgh;
- University of Canterbury, New Zealand;
- University of Glasgow;
- University of Lincoln
- University of Lincoln;
- University of Nottingham
- 19 more »
- « less
-
Field
-
the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
-
electrophysiology data obtained through collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in
-
that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
-
. The project integrates synthetic organic chemistry, kinetic analysis, automation, and machine learning to establish next-generation mechanistic workflows for asymmetric organocatalysis. The project advances
-
, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
-
, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
-
modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
-
and machine learning models. To be successful in this role, you will have excellent communication skills and written English, strong quantitative and analytical skills, the ability to work creatively
-
application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal
-
About the role – In this post, you will join a collaborative BBSRC-funded project focused on using metabolomics and machine learning to predict lameness outcomes in dairy cows. A typical day may