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sciences, computer science, machine learning, and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing or evaluating methodologies
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 22 days ago
-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Open Postdoc
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our
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of Mechanical and Aerospace Engineering at UCSD invites applications for a postdoctoral fellowship funded by the Gordon and Betty Moore Foundation focused on the topic of machine learning-based ocean state
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for Catalysis and Organic Chemistry at the Department of Chemistry. The group has extensive experience in computational modelling, reaction mechanisms, and machine learning for catalyst design and discovery. Nova
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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modeling approaches-including machine learning (ML), hydrologic and energy systems simulations, and scenario forecasting-to evaluate dynamic energy-water futures and resilience strategies for diverse Idaho
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for machine learning models to optimise membrane properties, structure, and fabrication. The fellow will play a key role in the experimental part of the project, including: Preparation and characterisation
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Cornell University, Electrical and Computer Engineering Position ID: Cornell-ECE-POSTDOC [#31375] Position Title: Position Type: Postdoctoral Position Location: Ithaca, New York 14853
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and