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how to benefit from recent research in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports
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on the applicant’s interests. Possible themes include: 1) the changing Meridional Overturning Circulation, from models and in situ data 2) using Lagrangian measurements to study ocean dynamics, for example transport
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methodology and spatiotemporal modelling for climate-sensitive infectious disease forecasting. The work will contribute to ongoing efforts to build real-time, operational early warning systems in collaboration
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models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks that support the human learning and improving
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and modeling studies of how natural aerosol (e.g. emitted from volcanoes) and anthropogenic aerosols (from inadvertent or intentional emissions) affect clouds, radiation, and climate. The main research
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Meridional Overturning Circulation, from models and in situ data 2) using Lagrangian measurements to study ocean dynamics, for example transport and spectral characteristics 3) ocean-bathymetry interactions
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highly accurate and efficient computational model by integrating the newly developed approaches into the numerical program developed in the ‘OceanCoupling ’ project. We would like the successful applicant
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Documented experience in at least one of the following areas: molecular cloning and mutagenesis in non-model bacterial species (ideally in a BSL-2 environment), mass spectrometry (ideally in combination with
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Division of NORCE Norwegian Research Centre there is a 3-year full time PhD research fellow position available within the field of modeling and simulation of CO2 storage. The position is for a fixed-term
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-Curie Doctoral Network NeuroNanotech : ‘Integrating Nanotechnology and Neurocomputational Modeling for Advanced Brain Interfaces’. The NeuroNanotech project aims to develop next-generation