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individuals, some tracked for more than a decade. You will be responsible for advancing and applying state-of-the-art probabilistic deep generative models, including conditional diffusion and flow matching
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The objective of this postdoctoral position, in
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, such as Bayesian approaches and fossilized birth–death models, to reconstruct robust phylogenies and estimate divergence times. It also investigates macroevolutionary dynamics, including variation in
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associated with phenotypic (biomechanical and metabolomics) traits. Estimate locus-specific effect sizes and quantifying genetically-driven phenotypic variations. Develop Bayesian models and/or deep learning
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Baku State University, Mathematics Research Lab Position ID: 3444-TTPOSTDOC [#26222] Position Title: Position Type: Tenured/Tenure-track faculty Position Location: Baku, Baku, Azerbaijan [map ] Appl
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 11 hours ago
process in a more systematic way. Science systems engineering focuses on ensuring alignment between the design and operation of an engineered system and its top-level science objectives, but there has not
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Description You will: * Lead MEG head-cast data collection for a visuomotor reaching/interception study, ensuring robust synchronization with video-based kinematics and eye-tracking, and enforce rigorous
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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