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the team. Each position will address a complementary research area within the project: 1. Quantum Control and Reinforcement Learning (CINN, Asturias) Develop AI-driven control strategies based
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The Chicago School of Professional Psychology | Chicago, Illinois | United States | about 23 hours ago
execute in-class activities to increase student engagement and reinforce key concepts and learning goals. Grade papers and assignments within two weeks of the assignment being turned in. As appropriate
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University of North Carolina Wilmington | Wilmington, North Carolina | United States | about 8 hours ago
Posting Details Posting Details Position Title 12-month Clinical Associate Professor, Master of Physician Assistant Studies External Link to Posting https://jobs.uncw.edu/postings/38742 College
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, and modern computing, including but not limited to causal inference/causal ML, data/population shifts, reinforcement learning, privacy and security, gen AI, and statistical learning, foundations of AI
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competition for external funding Concentration: Candidate with expertise in research and teaching in reinforcement learning, digital twins, generative AI, AI for drug development, and computation and spatial
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strategies, including spotbeam, satellite, and inter-satellite link handovers, and suggests the use of AI and cross-layer techniques. Reinforcement learning approaches have also been proposed to improve
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to apply Website https://www.academictransfer.com/en/jobs/359150/phd-position-digital-ai-chip-de… Requirements Additional Information Website for additional job details https://www.academictransfer.com
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doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline Experience in causal inference, decision-making, or reinforcement learning research
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, reinforcement learning, robust or explainable models). • Knowledge of Network Digital Twin concepts. • Experience working with large, real-world datasets and building reproducible pipelines (data quality, missing
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. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and reinforcement learning approaches that adapt lineup presentation in real time based