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, tensor analysis, and network science to foster the professional development of team members. Qualifications and experience essential PhD in Applied Mathematics in the fields of Numerical Linear Algebra
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree from a recognized institution (no more than 5 years since completing the doctoral degree
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the Job related to staff position within a Research Infrastructure? No Offer Description Description The Division of Engineering and the Center for Interacting Urban Networks (CITIES) at New York
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University of California Riverside School of Medicine | Riverside, California | United States | 2 days ago
Riverside. The Obenaus laboratory undertakes a range of studies on how brain injury temporally evolves in both adolescents and adults. For these positions, we are interested in how the cerebrovascular network
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PhD degree in Mathematics, statistics or theoretical computer science. The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be
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journals; - Participate in the life of the Center, contributing to scientific meetings; - Develop collaborations within the CBG network; - Offer short courses or minicourses; - Interact with graduate
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of a novel detection technique. - activities: The work will involve experimental investigations using advanced single molecule detection tools. The net monthly salary is between €2,200 and €2,380
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these degradation pathways. Join our team! Decoding Light-Induced Degradation through Molecular Networking and Data Science Many organic compounds change under the influence of (UV) light. This is beneficial in water
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interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine learning and neuro-symbolic AI (e.g. neural nets