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or application. Strong technical expertise in one or more of the following areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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infrastructure, with the state-of-the-art reinforcement learning and generative AI, to detect, prevent, and preemptively mitigate intelligent attacker vectors. Supportive Mentoring: The postdoc will be guided by
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opening of our state-of-the-art Molecular Sciences Building . We recently completed the strategic expansion of our academic staff, and we continue to invest in teaching and learning excellence . As part of
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Schemes of Service: Research Division: Engineering Employment Type: Fixed Term As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will
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problems, scale solutions, and drive impact. Real Learning for Real Impact. To reinforce our team, we are looking for a Research Fellow: Natural scientist or engineer wanting to make a difference in
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. Research Focus: Experience with machine learning, reinforcement learning, VLMs, or the creation of rigorous datasets and evaluation protocols is highly preferred. Appointment Details Term: Initial
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with remote sensing data (satellite, aerial, hyperspectral, SAR, LiDAR) Computer Vision Natural Language Processing Remote Sensing Machine Learening and Deep Learning Reinforcement Learning Large
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National Center for Toxicological Research (NCTR) | Jefferson, Arkansas | United States | about 1 hour ago
, gaining broad exposure to the design and interpretation of in-vivo toxicity studies and mechanistic endpoint integration. Learning Objectives: You will receive structured training in toxicity and