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Field
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist to develop hybrid models for sea ice that combine coupled climate models and machine
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or OpenFOAM Demonstrated experience in computational fluid dynamics and design optimization. Proposed Start Date: June 2, 2025 Proposed End Date or Length of Term: May 30, 2026 Number of Months Employed Per
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the CuDisc code (developed in-house at Imperial for modelling dust dynamics and dust-gas interactions, together with radiative transfer, in protoplanetary discs), coupled to N-body simulations of planetesimals
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. Application deadline: 22 June, 2025 For questions, please contact: Associate Professor Xin Zhao, Fluid Dynamics xin.zhao@chalmers.se *** Chalmers declines to consider all offers of further announcement
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machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital co-simulation
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3D micro and nano-imaging. Close collaboration with the interdisciplinary team at DTU Construct, DTU 3D Imaging Centre, and DTU Compute, particularly the other “MathCrete” postdoc at DTU Compute (see
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A knowledge of fluid dynamics is recommended. Personal characteristics Working as a Postdoctoral fellow requires that you The successful applicant is a highly competent, motivated and ambitious
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experience or equivalent Expertise in aerosol microphysics modeling, parcel-model simulation, fluid dynamics simulation, or related computational experience A strong track record of independent research and
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the National Center for Atmospheric Research (NCAR) and the National Oceanic, Atmospheric Administration's Geophysical Fluid Dynamics Laboratory (NOAA GFDL), and will foster new collaborations around ML/AI with
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focuses on building scalable, accreditation-ready analysis workflows to detect and classify microplastics in complex sample types such as drinking water, plant-based beverages, and biological fluids. As a