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Field
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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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imaging, X-ray tomography/radiography).
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performance programs. As a PhD candidate in this project, you will: Investigate muscle physiology using advanced techniques such as MRI-DTI, ultrasound imaging, muscle stimulation, and electromyography (EMG
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at least 2 other PhD students and one postdoc within the project. While this PhD project will focus on sustainable membrane production, others will focus on advanced functionalities and on upscaling
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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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developing state-of-the-art methods in network analysis, connectomics, and computational neuroscience. You will collaborate within a motivated, multidisciplinary team of PhD students and postdocs with
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the molecular simulation PhD project at TU Delft and a another postdoc/PhD at WUR who will work on the sensory performance of the formed milk fat globule mimics. You also possess: an MSc degree in physical
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simulation PhD project at TU Delft and a another postdoc/PhD at WUR who will work on the sensory performance of the formed milk fat globule mimics. You also possess: an MSc degree in physical chemistry
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metabolism and performance. Working across in vivo, ex vivo, and in vitro systems, you will integrate advanced cardiac imaging, metabolic flux analysis, and biochemical profiling to connect molecular damage
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of the PhD community of Naturalis and Biosystematics (WUR)— a close-knit group of PhD candidates and postdocs with whom you can share experiences and participate in training sessions, workshops, and