Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- UNIVERSITY OF VIENNA
- University of Oxford
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University College Cork
- AALTO UNIVERSITY
- Queen Mary University of London
- Queen Mary University of London;
- University of London
- University of Oxford;
- ;
- Imperial College London
- Lancaster University
- University College Dublin
- University of Reading;
- 4 more »
- « less
-
Field
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
characterising chronic diseases and disease patterns from electronic health record (EHR) data through the development of advanced deep learning methodologies based on state-of-the-art foundation models. You will
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
Schemes of Service: Faculty Division: Business, Communication and Design Employment Type: Fixed Term At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical
-
at APC Microbiome Ireland. Together they will be focused on interdisciplinary approaches to unravel how the microbiome communicates with the brain. The successful candidate will bring deep expertise in
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
/deep learning-based medical image analysis methods for computerised tomography scans (CT scans). Key applications of such ML/DL methods are illustrated by our prior research (PMIDs 33913675, 33234786
-
studies (GWAS), fine-mapping, colocalisation, polygenic risk scoring, and Mendelian Randomisation; and (ii) deep phenotyping of multi-modal cardiovascular imaging (MRI, CT, echocardiography) from large