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Field
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to transform the way environmental and social data are integrated into fisheries management by combining advanced ocean forecasting, species-specific modeling, and social science to develop adaptive strategies
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candidate will refine the physical processes represented in the regional model, with particular attention to improving land–sea coupling and sediment–water interactions. Building on recent progress in both
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to investigate the parameters that control the triggering, speed and geometry of magmatic intrusion events at mid-ocean ridges, using dynamical models constrained with seismo-geodetic observations
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analyze a suite of high-resolution regional ocean biogeochemical model simulations to provide actionable information to decision makers as part of NOAA’s Changing Ecosystems and Fisheries Initiative (CEFI
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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-atmosphere dynamics. We will build an AI-enabled modeling system that couples a GPU-optimized ocean model with a biogeochemical module and AI-based, kilometer-scale atmospheric forecasts. This system will
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to lead an investigation exploring the ability of recently developed global earth system models to simulate coastal sea level across sub-annual timescales. This work will leverage a suite of coupled models
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marshes and statistical and chronological models that allow reconstruction of the magnitude and timing of past relative sea-level (land level) changes at the Alaska-Aleutian subduction zone. A further
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and fossil diatom datasets from Alaska-Aleutian coastal marshes and statistical and chronological models that allow reconstruction of the magnitude and timing of past relative sea-level (land level
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our research team at HKU working at the forefront of ocean remote sensing and global change monitoring. Our work combines satellite data, physical models, and AI methods to understand ocean dynamics and