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to encourage knowledge exchange. In addition, we have a track record of excellent academic placements. Around 80% of our graduated PhD’s started their first job in academia, and over 50% landed a job at a top
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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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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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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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gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); giving mathematical proofs of their correctness and efficiency; building state
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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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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