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simulations; (ii) diagnose and attribute the physical mechanisms driving inter-model differences in recovery; and (iii) design and run targeted mechanism-denial experiments (e.g., in CESM and related Earth
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microbiology. This will include performing fertilisation experiments, measuring plant traits, including above-ground and below-ground, and the collection and analysis of plant and soil microbiomes. The role
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on Smart Fibre-Optic High-Power Photonics (HiPPo). The HiPPo programme (https://www.hippo-laser.co.uk/ ) is focused on understanding how to control the properties of fibre lasers, to go beyond the “fixed
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of Ocean and Earth Science at the University of Southampton. The successful candidate will be part of the ‘Accurate projections of climate recovery from a combination of historical data, simple models and AI
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cutting-edge data from new ground and space-based facilities to address fundamental questions in galaxy evolution? Then this might be the right opportunity for you. The Astronomy Group within the School
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, including above-ground and below-ground, and the collection and analysis of plant and soil microbiomes. The role involves extended fieldwork in Mexico and close collaboration with UK- and Mexico-based
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physics, Aerodynamics, Mechanical, or Aerospace Engineering. Essential criteria include experience in numerical modelling of plasma flows and discharge, with desirable expertise in plasma diagnostics and RF
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across Linux and other operating systems. Experience integrating genomic data with structured clinical data and familiarity with clinical ontologies (e.g., HPO, SNOMED CT, ICD‑10). A solid understanding of
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About the Role We are seeking to recruit a Research Fellow in AI for Climate Modelling to become a member of the School of Ocean and Earth Science at the University of Southampton. The successful
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We are seeking a highly motivated Research Fellow to join the Hydroclimatology Group at the University of Southampton, led by Professor Justin Sheffield. You will lead the development and application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological...