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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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scientific machine learning-based strategies for the discovery of self-similarity laws, use of quantised local reduced order models, and real data assimilation. You will be assimilated, jointly
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₂ incubations with phytoplankton will test whether phytoplankton-derived methane is assimilated into methanotroph biomass. Together, these approaches will clarify how benthic and pelagic carbon pathways intersect
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physiology and nutrient assimilation. Controlled feeding experiments will trace trophic transfer into zooplankton and higher consumers, generating quantitative coefficients for ecological risk models. Finally