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that do not have this background, are strongly motivated to acquire the relevant skills during the early phases of the PhD, supported by training and in close collaboration with experts in single-cell
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chapters, and shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members. Contribution
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shorter contributions such as blogposts. Some of these will be co-authored pieces with the principal investigator (Prof Van Calster), the postdoc and other team members. Contribution to the outreach
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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that truly understands its environment. You have a master's degree in Computer Science, Artificial Intelligence or similar. You are interested in Logic, Machine Learning, Knowledge Graphs, Stream
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches
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, microscopy, spectroscopy, pore-structure characterization, geochemistry. You are interested in interdisciplinary research at the interface of materials science and microbiology and are motivated to acquire
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considered a strong asset. Experience with Deep Learning and Artificial Intelligence is considered a plus. Excellent proficiency in the English language is required, as well as good communication skills, both
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plasticity consists in learning dendritic, synaptic, or axonal temporal delays to enrich the network’s spatiotemporal dynamics. This research project will thus explore how these different mechanisms can be
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: Master’s degree in Electrical Engineering Ranked within the top 10% of their class in MSc and BSc, and have exceptional grades Good background in deep learning with familiarity in model training, inference