Sort by
Refine Your Search
-
Listed
-
Country
-
Employer
- Nature Careers
- SciLifeLab
- VIB
- ;
- Hannover Medical School •
- Swinburne University of Technology
- University of Antwerp
- ; City St George’s, University of London
- Chalmers University of Technology
- DAAD
- Leibniz
- University of Southern Denmark
- ; St George's, University of London
- ; University of Leeds
- Aalborg University
- Curtin University
- Dresden University of Technology •
- Empa
- Ghent University
- KNAW
- Karlsruhe Institute of Technology •
- Ludwig-Maximilians-Universität München •
- Technical University of Munich
- University of Groningen
- University of Münster •
- University of Nottingham
- University of Twente
- University of Tübingen •
- 18 more »
- « less
-
Field
-
scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
-
Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
-
sensor technologies—including but not limited to biomedical radar—to improve fall risk prediction and support rehabilitation in healthcare settings. About us At the biomedical electromagnetics group , we
-
/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
-
(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
-
Biology, Biomedical Imaging, Biochemistry, Physics, or a related field A strong interest in biomedical imaging, contrast agent development, immune cell tracking, and data analysis Previous experience with
-
to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
-
. The mission of Aithyra is to fundamentally transform biomedical science by embedding artificial intelligence as a co-pilot throughout the entire scientific process-from hypothesis generation and experimental
-
This project aims to enhance best practices in strain quantification for biomedical applications, facilitating the transition of image-based measurement methods from laboratory research to clinical
-
of biomechanics, biomedical imaging, and neuromuscular physiology in an interdisciplinary and international environment. Limited teaching within biomechanical engineering can be expected, but also in other study