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Field
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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
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, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision, digital pathology, whole-slide image analysis, self-supervised learning, foundation models, multiple
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related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with deep learning, computer vision, medical image analysis
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distribution in regenerative biomaterials. The candidate will receive interdisciplinary training in biomaterials research, advanced imaging, quantitative image analysis, data processing and machine-learning
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scale radar datasets with high quality labels remain scarce. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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, and machine learning methods, choosing the approach that best fits the scientific question. Investigate systematically what information is contained in imaging data, how it can be extracted, and how
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reconstruction, processing and quality-control tools in a transparent and reproducible way. You will have the opportunity to: Develop a modern, open and extensible software ecosystem for optoacoustic image
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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planning and decision-making. Working with real-world data from Alliander, you will publish at leading machine learning venues while building tools with tangible impact on the Dutch energy sector. The Dutch