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Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation of multi-physics systems, especially fluidic problems Expertise in generative
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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes
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optimization of multi-modal LLMs. Investigate and implement methodologies to ensure AI authenticity, accountability, and the integrity of digital content. Develop and refine machine learning and deep learning
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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field of engineering Research experience in one or more of the following: geometry, optimization, dynamical systems, mechanics, probability and statistics, data science, machine learning Evidence of
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, or other related fields. Solid Mathematical skills. Experience in implementing algorithms for machine learning and natural language processing-related applications (It would be good if the candidate can also
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topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI, and AI Safety. Key Responsibilities: Manage own academic
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian
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of analyzing large-scale population data. Experiences working with electronic health records (desirable). Understanding of clinical informatics approaches (e.g., machine learning, Bayesian statistics) and