Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Amsterdam UMC
- CNRS
- Eindhoven University of Technology (TU/e)
- Tilburg University
- University of Oslo
- University of Texas at El Paso
- ARCNL
- Delft University of Technology (TU Delft)
- Fondazione Bruno Kessler
- Foundation for Research and Technology-Hellas
- Hannover Medical School •
- Inria, the French national research institute for the digital sciences
- KNAW
- Monash University
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Technical University of Munich
- The Rosalind Franklin Institute
- The University of Newcastle
- University of Amsterdam (UvA)
- University of Nottingham
- Vrije Universiteit Brussel
- 12 more »
- « less
-
Field
-
computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
-
reconstruction of the underlying three-dimensional scene. Within this PhD project, you will develop an end-to-end incoherent holographic imaging system. You will investigate models for the propagation
-
, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal microscopy and SEM, with computational image analysis, computer vision and machine
-
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
-
to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
-
learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy (PEM), and focused ion
-
PhD student (f/m/d) who wants to take ownership of the software ecosystem behind our computational imaging research – from experimental reconstruction algorithms used inside our lab to robust tools used
-
computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
-
procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop probabilistic digital twins of coronary arteries. The goal is to create patient-specific
-
accuracy. By combining global visual information from an ophthalmic microscope with local images acquired by a miniature ophthalmic endoscope, the project will investigate how shadow-based visual cues can be