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the Sustainability Institute at University College Cork. UCC invites applications for a Postdoctoral Researcher specialising in industrial decarbonisation modelling to support the EU-funded FLARE project. The role
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industrial decarbonisation modelling to support the EU-funded FLARE project. The role will lead the technical development and integration of bottom-up, organisation-level decarbonisation models for energy
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
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and laboratory measurements with modeling to test the broad hypothesis that the depth distribution of common macroalgal species in southern California is predictable based on measured functional traits
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particle formation for atmospherically relevant molecules. ORCTOOL (Organic Cluster Tools) aims to create a toolbox for understanding and cost-effectively predicting the rate of massively multicomponent
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 2 months ago
. Description: Ames scientists are actively involved in theoretical computation of extrasolar planet atmospheres, predicting exoplanet spectra, conducting and interpreting exoplanet observations, planning future
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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at two levels: SAACD Component: This is a UAV made up of hardware and software sub-systems, capable of observing, predicting, deciding and reconfiguring itself to fulfil its mission (e.g. surveillance
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to pre-train a common GNN backbone model capable of predicting electronic, structural, and thermal quantities while leveraging underlying symmetries for computational efficiency. There will be a