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the Distributed AI for dependable cyberSecuritY (DAISY) Unit, of FBK-CS, which focuses on developing novel AI-driven methods for cyber threat detection in distributed computing environments, with a particular
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. They manifest as explosive bursts and sustained emissions of gamma rays generated by lightning and thunderstorm activity. This project aims to unlock the mysteries connecting TGFs, FGFs, glows, and lightning
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experience from or competence in radiation detectors (design, construction, operations or data analysis) is a requirement. A general knowledge of programming languages aimed at scientific data analysis and
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, etc.) to support tracking of project/program activities (e.g. incentive distribution; sending, receiving, and cleaning audio files for transcription, etc) Support search and set up procurement
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of group's research is focused on source apportionment of size-resolved AA based on mass concentration data, chemical composition and particle number size distribution. The application of receptor models using
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development of transferable learned representations from large-scale public omics data and metadata, and the software infrastructure needed to build, maintain, and distribute them at community scale, including
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a key role in building and integrating of AI agents into gaming scenarios (e.g., gameplay, interactions, procedural content generation, dynamic narratives), and integrating a multimodal detection
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Work Environment Entrepreneurial culture Affiliation with Texas A&M University students and faculty Ability to work with nationally recognized experts Ability to help train the next generation of
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scalable data pipelines and workflows for large, heterogeneous datasets in scientific and engineering domains Contribute to research in areas such as synthetic data generation, simulation environments, and
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with renewable energy generation, the utilization of EVs as distributed energy storage through Vehicle-to-Everything (V2X) technologies, and coordinated control mechanisms—such as aggregators—to mitigate