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backgrounds in statistics, computing, machine learning (ML), and genetics and genomics, with a focus on large-scale genetic, genomic, and phenotype data. The work will involve both methodological research
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candidates will have access to local computing facilities and will have the opportunity to interact and work with researchers in both the Harvard College Observatory ( HCO ) and the Smithsonian Astrophysical
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learning; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic
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Professor (all ranks) in Secure and Trustworthy AI Systems Arizona State University: Office of the University Provost: Ira A. Fulton Schools of Engineering: School of Computing and Augmented
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that machine learning AI approaches will prevail as a generic computing paradigm, and will find an ever growing range of practical applications, many of which will have to do with various aspects of
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that machine learning AI approaches will prevail as a generic computing paradigm, and will find an ever growing range of practical applications, many of which will have to do with various aspects of
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ensembles of diverse opponents.” Neural Computing and Applications (2025). https://doi.org/10.1007/s00521-024-10511-9 de Arruda, H. F., Gracia-Lázaro, C., Aleta, A. and Moreno, Y. “Collective
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ensembles of diverse opponents.” Neural Computing and Applications (2025). https://doi.org/10.1007/s00521-024-10511-9 de Arruda, H. F., Gracia-Lázaro, C., Aleta, A. and Moreno, Y. “Collective
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Other Graz, Austria Apply University of Graz, Austria Department of Mathematics and Scientific Computing Heinrichstrasse 36 8010 Graz Austria The address could not be found. Your Responsibilities
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performance and funding. Resources for research expenses including publication charges, related conference travel, workshops, and computing resources will be available. Further information about the BHI may