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security. Expertise in data-driven modeling (ML for energy, forecasting, anomaly detection) and physics-informed learning. Real-time/HIL or embedded control experience (e.g., OPAL-RT, RTDS) and laboratory
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dependable large-scale software systems, integrating expertise in: Software Engineering Machine Learning & MLOps Robotics & Cyber-Physical Systems Cloud & HPC ecosystems Interdisciplinary research. As a
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, research, and public service. Job Description Purpose: The Department of Electrical and Computer Engineering and AggieFab Nanofabrication Facility at Texas A&M University seeks a Postdoctoral Researcher to
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verbal communication skills. Ability to manage complex projects. Work Location: Main Campus – College Station, TX. About The Department of Electrical and Computer Engineering: The Department of Electrical
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Engineering or a related field The ideal candidate should have some knowledge and experience in the following topics: Software Cybersecurity Software Testing and Analysis Machine Learning and Multimodal Large
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world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and
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gain real-world experience through research, internships, and industry-sponsored capstone projects. With a versatile curriculum spanning software, systems design, nanofabrication, and machine learning
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research on cyber-physical electric grid security, data-driven and AI-based energy management, and electric grid resilience. Develop and validate simulation models, design hardware-in-the-loop test setups
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-generated scenarios or machine learning-driven attack/defense strategies; Experience in developing comprehensive security assessments, producing technical reports, and contributing to toolkit documentation
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, that can be documented by a publication record in relevant venues. Solid understanding of state-of-the-art embedded machine learning techniques. Experience in system-level programming, developing prototype