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of the Department of Mathematics at Radboud University (Nijmegen, Netherlands), and join the research group of Laura Scarabosio, funded by the NWO Vidi programme ’Taming Frequency in Bayesian Inverse
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domains. Develop hybrid AI architectures combining symbolic domain knowledge, real-time data streams, and probabilistic inference. Design and evaluate decision support tools capable of interacting with
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to improve R&D efficiency, and the influence of investors and other external actors on entrepreneurial outcomes. Our research also examines decision-making under uncertainty, including the use of Bayesian
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collaborative. Proxy data will amongst others infer the stable isotope composition of foraminifera and mollusc shells, since not much is known about the changes in temperature and isotopic composition
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the Netherlands’ national bird, the black-tailed godwit. Join our team! We seek a quantitative, ecology-minded PhD candidate to expand our state-of-the-art Bayesian Integrated Population Model (IPM
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staff position within a Research Infrastructure? No Offer Description Are you excited about causal inference, real-world data, and methodological innovation? Join us to explore how the integration
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studying k-space (Fourier domain of the image in which the acquisition is performed) samples from over the entire time series, a neural-implicit representation can infer what the full k-space should look
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others infer the stable isotope composition of foraminifera and mollusc shells, since not much is known about the changes in temperature and isotopic composition of the waters flooding the Netherlands and
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series, a neural-implicit representation can infer what the full k-space should look like at any given time. This way, we will achieve an image quality of quantitative MRI as if conventional MRI were being
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target population. Such “opportunistic” data pose significant challenges for making valid inferences about population-level environmental metrics such as soil properties, biomass stocks, or map accuracy