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to accurately predict and simulate the effect of the best possible treatment for a patient. We foster an open culture with generally accessible collaboration. Moreover, we strive for equal opportunities
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cognitive operations in existing data, label these operations, and then train transformer models to predict sequences of operations in unseen data. Please see Weindel, Borst, & van Maanen, eLife, 2025
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datasets to identify and characterize microbial and plant-derived biosynthetic pathways, predict their ecological functions, and reconstruct the (co-)evolutionary dynamics of traits such as microbiome
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will contribute to a scientifically challenging and industrially relevant topic, with the aiming at predictive relations between measurable wafer surface properties and bonding performance for next
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technologically relevant problem. Project goal The project aims to develop a predictive, experimentally grounded understanding of how rapid solidification and ambient pressure shape molten-tin droplet impacts, and
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Position Data Science and Artificial Intelligence in Cardiac Electrophysiology Our goal: the development of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically
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of multimodal prediction models to detect and predict atrial fibrillation (AF) and other clinically relevant cardiac rhythm patterns, predict disease progression and treatment response, and support personalised
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scientifically challenging and industrially relevant topic, with the aiming at predictive relations between measurable wafer surface properties and bonding performance for next-generation 3D chip integration
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from world-leading European research institutions and companies. You will work closely together with researchers across the consortium to predict and edit targeted gene promoters, to design relevant
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, to eventually capture them in physical-chemical models that predict the impact of acidification on marine P cycling. The PHOSFLUX project is a collaboration between the NIOZ (dr. Peter Kraal) and Utrecht