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Field
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optimal performance The PhD student will be supervised by Ida-Maria Sintorn, Professor in digital image processing, and Jens Sjölund, Assistant Professor in AI at the Department of Information Technology
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, longitudinal patient and population registries and biobanks. Project description The development of artificial intelligence (AI) and computerised image processing in combination with advanced digital microscopy
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, including Machine Learning & Artificial Intelligence, Colour & Imaging, Computer Vision, Graphics, Data Science, Health Computing, Computational Biology, Cyber Intelligence and Networks. We collaborate with
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, moisture‑related defects and narrow processing windows, limiting their wider adoption. This PhD will address these challenges through a combination of experimental materials science, advanced
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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing
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position The PhD student will: Develop machine learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch
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predictive mechanisms such as a digital twin. The candidate of this research may focus on the design of the concept and prototype of such research environment for supporting organisations towards AI Act
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develop a digital twin of the PFA cure process, combining mechanistic modelling with neural‑network‑based prediction of complex behaviours such as void formation and brittleness. In parallel, you will
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Delft. In this team, consisting of several PhD students, postdocs and undergraduates, we focus on developing new concepts for 3D imaging of nanostructures, using light. Job requirements A masters degree
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. Proficiency in English (written and oral) are required. Previous experience in (non)linear optics, spectroscopy, microscopy, nanophotonics or digital imaging is desired but not a must. Finally, the ability