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candidate will contribute to cutting-edge research in optimal control theory, differential game theory, mean-field game theory, and mean-field-type game theory, developing advanced mathematical frameworks and
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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-VLMs, tiny-VLAs; Agentic-AI systems; and Robust Generative AI targeting hallucination, safety and security issues. Besides robustness, systems should also be optimized for high performance and energy
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, systems should also be optimized for high performance and energy-efficiency to realized efficient Embodied/Edge-AI implementations. A key focus will be on investigating novel methods and their full-system
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prototyping for real-world use-cases, and building TRL-4/5 prototypes and actively optimizing the engineered solutions. The candidates will work in a multidisciplinary environment consisting of PhD-level
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications, reports and seminars The selected candidate will be part of the Foundations Cluster of CIDSAI and
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-property-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications
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energy-related materials. The successful candidate must hold a PhD in Chemistry, Materials Science, Chemical Engineering, Physics, or a closely related discipline, within less than 5 years post receiving
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-VLMs, tiny-VLAs; Agentic-AI systems; and Robust Generative AI targeting hallucination, safety and security issues. Besides robustness, systems should also be optimized for high performance and energy