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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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://dimag.ibs.re.kr/ Successful candidates for the research fellowship positions will be new or recent PhDs with outstanding research potential in all fields of Discrete Mathematics with emphasis on Structural Graph
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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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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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optimization, optimal transport, mathematical finance, or machine learning; • A track record of high-quality research, demonstrated through publications, preprints, or a strong PhD thesis; • Experience
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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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. 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
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limited to: Computational and applied mathematics; artificial intelligence for sciences (especially biomedical science, brain science, and neuroscience); data science and machine learning; modeling
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towards better understanding what replication can mean for qualitative research. Last but most topically, the theme of replication raises a host of questions in relation to machine learning and artificial