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Field
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prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple scientific areas, CALS can address challenges and opportunities
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, equitable, trustworthy, and context-sensitive. Multi-agent architectures where multiple AI systems collaborate, negotiate, and adapt to model complex human learning processes and support group work
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, architecture simulation and prototyping for future computer systems and their applications, to reach multiple orders of magnitude better performance, energy-efficiency, and total-cost-of-ownership. We
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technologies across multiple domains. • Experience with SIEM, SOAR, EDR, DLP, CASB, vulnerability management, and cloud security platforms. • Proficiency in scripting and automation (e.g., Python, PowerShell
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to multiple and diverse audiences. • Design and develop institutional dashboards (e.g., enrollment, retention, graduation rates) using tools like Tableau/Power BI for campus leadership and external reporting
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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: understanding how they can be used to reconstruct sensitive client data and designing robust defense mechanisms to protect against such threats. Context Federated Learning (FL) enables multiple clients
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distribution networks, considering solutions such as FL Energy's OperatorFabric.; - Study human-machine interaction methodologies that reduce the complexity of supervising multiple distribution networks
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, ensuring data integrity, performance, and scalability Develop and maintain Python-based data processing pipelines and backend services Build and enhance web applications and user interfaces for data
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Python for automation, working with cloud platforms (Azure preferred, GCP or AWS), and supporting modern analytics engineering with tools like DBT. Design and implement enterprise data integration