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industry partners. Design, implement, and validate advanced reinforcement learning models. Utilize reinforcement learning and evolutionary algorithms to discover new chemical materials. Publish and present
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, computer science, architecture, and engineering to develop scalable, data-informed solutions in sustainable design, construction, and energy management. The Cluster aims to modernize—and ultimately revolutionize
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algorithms, clinical decision support systems, and population health management platforms. Evaluate emerging technologies in clinical informatics and provide strategic recommendations for their adoption within
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values diversity acting as a role model and fostering an inclusive working culture Person Specification A First degree with strong quantitative and informatics component e.g. biological, biomedical
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, nearly 12,000 faculty and staff and 350,000+ living alumni, UC combines a Top 35 public research university with a physical setting The New York Times has called “the most ambitious campus design program
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, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies of statistics, logic and machine learning
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. This insight opens the door for enjoying the real world. The candidate further develops efficient and robust algorithms for realistic settings in terms of data and computing resources and collaborates to address
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information about AbbVie, please visit us atwww.abbvie.com . Follow @abbvie onX ,Facebook ,Instagram ,YouTube ,LinkedIn andTik Tok . Job Description Program Overview AbbVie needs outstanding individuals willing
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Engineering or equivalent from a recognized University. Major in Power Engineering will be advantageous. Minimum 5 years of relevant experience in power electronics, drives, control algorithms, optimization
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning