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the damaging effects of tau. Building on our discovery of this novel intrinsic neuroprotective mechanism, the GVBoost project aims to identify druggable targets and therapeutic compounds that can enhance this
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of the estimand framework and target trial emulation can improve the design, analysis, and interpretability of observational studies for clinical and regulatory decision-making. Your job In this PhD project, you
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A better future for everyone. This ambition motivates our scientists in executing their leading research and inspiring teaching. At Utrecht University , the various disciplines collaborate intensively towards major strategic themes . Our focus is on Dynamics of Youth, Institutions for Open...
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the next. Your models will first be used to analyze completed experiments and identify trends, and later integrated into active learning and Bayesian optimization frameworks to suggest which experiments
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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varying material properties. The resulting response will be analyzed using techniques such as Monte Carlo simulations. Identifying the variability of the model parameters using Bayesian inference
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programming techniques (e.g., techniques for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference
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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 Wave Scattering’. Inverse wave
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with a hybrid-electric propulsion system, targeted for entry into service by 2035. Given the rapid evolution of various battery technologies, it is currently uncertain which specific technology will be
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many