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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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or later, but no later than December 1, 2027. This is purely a research position and will have no teaching duties. However, if desired, successful candidates may have the opportunity to teach at KAIST. A
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collaborative and interdisciplinary activities. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Applied Mathematics or equivalent. Strong academic background in machine learning
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experiments, machine learning libraries, or reinforcement learning environments; • Good written and oral communication skills; • Ability to work independently and collaboratively in an interdisciplinary
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• PhD in Mathematics/Statistics/CS. Skills: • Proficiency in reinforcement learning, quantitative finance, risk management, and machine learning. • Strong programming skill in Python
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develop statistical, mathematical, causal-inference, simulation, and machine-learning methods using large-scale flight trajectory and operational datasets. Research may cover airport surface congestion
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top-tier conferences and journals related to AI, security, privacy, digital forensics, or trustworthy computing. Solid background in Machine Learning, Digital Forensics, Security, and AI Generation
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differential geometry, algebraic geometry, etc.) or for computer science (such as machine learning, linear logic, etc.).While the position start date is flexible, the successful applicant must have completed
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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mathematical structures and computational algorithms underlying modern machine learning and artificial intelligence. Relevant themes include geometric and algebraic methods for learning, structure-preserving