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- Delft University of Technology (TU Delft)
- Delft University of Technology (TU Delft); yesterday published
- Amsterdam UMC
- Delft University of Technology (TU Delft); 3 Oct ’25 published
- Eindhoven University of Technology (TU/e)
- Elestor BV
- University Medical Center Utrecht (UMC Utrecht)
- University Medical Centre Groningen (UMCG)
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); Published today
- University of Amsterdam (UvA); Published yesterday
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- Utrecht University; Published yesterday
- Vrije Universiteit Amsterdam (VU)
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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and
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learning has strong potential for computer vision, from hyperbolic image segmentation [2] to hyperbolic tree embeddings [3] and hyperbolic vision-language models [4,5]. [1] Nickel, Maximillian, and Douwe
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preferably with data analysis and machine learning (e.g., Python, AI frameworks). You have strong analytical and problem-solving skills, with the ability to translate complex clinical processes into structured
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physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated against experimental observations Bridging scales from pore-level
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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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, you will have experience with acquisition and processing of tomographic images, rock mechanics, machine learning and/or numerical modelling. Given the highly diverse nature of the research groups
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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appropriate benchmarks and evaluation metrics to assess the developed models. Write a thesis consisting of four published articles in the fields of Information Retrieval, Natural Language Processing and Machine
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ