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About the Opportunity The Network Science Institute (NetSI) at Northeastern University is a world-leading center for research and education in network science. NetSI faculty and researchers
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the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five centers and institutes
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the outstanding scientific environment of the Beutenberg Campus providing state-of-the-art research facilities and a highly integrative network of life science groups. We offer a multifaceted scientific project
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IPCC reports opportunities for cooperation with PIK’s national and international network of leading climate & energy research institutes and think tanks insights and experience in strategic research
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support society's need to regulate fishing. The section is internationally recognized and has a strong collaborative network within the development of fisheries technology. We offer a rewarding and
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 9 days ago
) Scientific and professional mentoring, strong support for personal career development, and access to trainings, networking opportunities, and other resources provided by the Max Planck Society Opportunities
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vibrant and interdisciplinary research environment at TUM Access to unique datasets and collaboration with a well-established European network Opportunities for professional development and academic career
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well as an extensive national and international researcher network. The department consists of nine research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757