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of hyperspectral and ultraviolet capabilities on NASA’s upcoming ocean color satellite missions, PACE, GLIMR and SBG, to infer biological and biogeochemical constituents from the derived optical properties
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computing (HPC) environments and include data assimilation techniques in a Bayesian framework. Under the guidance of a mentor, the participant will identify and integrate multiple data streams into the model
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received by November 1, 2025. Preferred skills: Demonstrated experience in modeling and applied statistics including machine learning, Bayesian statistics, multivariate statistics, model assisted estimation
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hierarchical, contextual knowledge for complex multi-modal scene understanding Neuro-symbolic architectures for representation, learning, reasoning and inference Biologically-inspired metacognitive AI paradigms
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approaches. Experience in programming in R, using GitHub, and doing Bayesian statistical analyses with the use of MCMC samplers such as JAGS, STAN, or NIMBLE. Point of Contact Justina Eligibility Requirements
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skills: Demonstrated experience in modeling and applied statistics including machine learning, Bayesian statistics, multivariate statistics, model assisted estimation, rarefaction, or wildland fire