Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- Imperial College London
- University of Birmingham
- Nature Careers
- UNIVERSITY OF SOUTHAMPTON
- KINGS COLLEGE LONDON
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Nottingham
- The University of Southampton
- UNIVERSITY OF SURREY
- University of Oxford
- King's College London
- QUEENS UNIVERSITY BELFAST
- University of Cambridge
- University of Leeds;
- University of London
- ; University of Oxford
- Aston University
- Birmingham City University
- CRANFIELD UNIVERSITY
- Lancaster University
- Lancaster University;
- Nanyang Technological University
- Nottingham Trent University
- Plymouth University
- Queen's University Belfast
- Queen's University Belfast;
- Technical University of Denmark
- UNIVERSITY OF MELBOURNE
- University of Bristol
- University of Glasgow
- University of Leeds
- University of Lincoln
- University of Liverpool
- University of Manchester
- University of Plymouth;
- University of Sheffield
- 27 more »
- « less
-
Field
-
qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
-
Jan. 2026, based in the University of Birmingham UK. This position will use further develop the novel AI/machine-learning (ML) approach in Chen et al. (2022 & 2024, Nature Geoscience ) and apply
-
, written, and oral communication skills in English. Exhibit strong organisational skills and the ability to meet deadlines and complete projects. Have expertise in machine learning and/or programming (highly
-
approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
-
spatial transcriptomics and imaging genomics projects, integrating bulk and single-cell RNA-seq datasets, and applying advanced statistical and machine-learning methods (AI/ML) to extract novel biological
-
looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather
-
, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
-
tools such as R, Python, or MATLAB as well as relevant machine learning frameworks Experience in statistical data analysis, and expertise in areas such as experimental design, linear/nonlinear models
-
Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
-
: Genomics, precision medicine, bioengineering, and health data science AI and Digital: Machine learning, robotics, digital health, and cybersecurity Defence and Advanced Manufacturing: Secure systems