Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- CNRS
- KU LEUVEN
- Inria, the French national research institute for the digital sciences
- International PhD Programme (IPP) Mainz
- NTNU Norwegian University of Science and Technology
- SciLifeLab
- Amsterdam UMC
- Delft University of Technology (TU Delft)
- ETH Zürich
- Forschungszentrum Jülich
- NOVA.id.FCT- Associação para a Inovação de Desenvolvimento da FCT
- NTNU - Norwegian University of Science and Technology
- Nicolaus Copernicus University
- Radboud University
- Technical University Of Denmark
- Technical University of Munich
- UNIVERSITAT POMPEU FABRA
- University Medical Center Groningen
- University of Antwerp
- University of Birmingham
- University of Cyprus
- University of Florida
- University of Göttingen •
- University of Nottingham
- University of Oxford
- Utrecht University
- Wageningen University & Research
- 17 more »
- « less
-
Field
-
models ranging from baseline approaches to graph neural networks. You will also oversee the open release of project datasets, models, code and documentation. The successful candidate will join Oxford's
-
on diverse topics ranging from how organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory. Activities and responsibilities The research group of Anton
-
Reference Number BAP-2026-500 Is the Job related to staff position within a Research Infrastructure? No Offer Description Modern embedded AI systems rely on Deep Neural Networks (DNNs) running on resource
-
of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
-
specific requirements: a) Experience in the application of data analysis and machine learning methods to scientific data (e.g. multivariate analysis, chemometrics, neural networks, classification
-
will join a community of exceptional scientists working on diverse topics ranging from how organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory
-
representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
-
or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark datasets and an
-
predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
-
further appears in the approximation of various dynamical problems by tensor networks and neural networks. In all these cases, the parametrization is typically irregular, meaning that the occurring linear