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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
: 277494287 Position: Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in
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) and Aurélien Quiquet from the Laboratoire des Sciences du Climat et Environnement (LSCE), an expert of the GRISLI model, in interaction with the PhD student on the ANR Delta project (coordinated by Y
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modeling. Positions may begin as soon as September 2025 . A Ph.D. in Mechanical and Aerospace Engineering, Civil and Environmental Engineering, Atmospheric and Oceanic Sciences, Geosciences, Computational
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to these changing conditions? The postholder will help to answer these questions using agent-based modelling (ABM) of social-ecological systems, drawing on archaeological and historical datasets collated through
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to specialize the memory management of several applications, including virtual machines. Running memory management policies in user space opens up new opportunities, particularly the integration of AI models
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The post-holder will join a team of investigators working on the NERC-funded Pushing the Frontiers grant ‘Influence of complex source and environmental source conditions on eruptive plume height
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human stem cells for in vitro modeling of neurodegeneration disease. Projects in the lab are at the interface of systems biology and bio-medical research. In particular, we aim at developing novel
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primarily involves simulation-based studies and computational modeling. Required Training and Other Conditions of Employment Every employee at Michigan Technological University will receive the following four
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Job Advertisement 2025-06 23.07.2025 At the Leibniz Institute of Atmospheric Physics (IAP), a full-time position (40h/week) in the Department “Modelling of Atmospheric Processes” is available as
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months. The candidate will hold a PhD in physics, geosciences, mathematics, or an equivalent field. Previous experience or demonstrated knowledge in palaeoclimate dynamics and climate modelling will be