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Field
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capabilities in federated learning frameworks, with emphasis on scalable, reproducible, secure, and extensible research software. Evaluate model performance, robustness, generalizability, fairness, privacy, and
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, bioeconomy research, and related fields. Assist in developing conceptual and analytical frameworks that bridge scientific, humanistic, and social scientific approaches to environmental research. Participate in
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the Twin Cities metro area While our salary ranges provide a framework, it is important to note that most of the time, the initial pay may not reach the maximum of the range. This approach ensures
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. • Familiarity with reinforcement learning and autonomous robotic control. • Experience with ROS (Robot Operating System) or similar robotics software frameworks. • Experience providing guidance or mentorship
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frameworks. L–H/H–L transition physics, pedestal evolution, or core-edge coupling. Analysis and validation using experimental data from tokamak facilities. Machine learning, scientific AI, neural-network
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adherence, within-host differences in infectiousness, and uncertainty quantification frameworks to evaluate HIV intervention scenarios. Minimum Requirements: A PhD degree is required. Preferred Qualifications
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computing and artificial intelligence. Areas of interest include, but are not limited to, the following: Quantum Machine Learning and AI: Develop novel quantum algorithms and computational frameworks
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National Energy Technology Laboratory (NETL) | Albany, New York | United States | about 12 hours ago
will engage in multiscale modeling and machine learning to develop reliable, efficient framework for predicting long-term creep and fatigue behaviors of heat-resistant structural alloys considering
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frameworks for inverter-dominated systems, identifying stability boundaries, and designing predictive mitigation strategies under both normal and adversarial (cyber-physical) conditions. A key feature of this
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of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI implementation. Ability to apply AI to interdisciplinary research or developing AI models