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interdisciplinary team focused on developing innovative numerical algorithms and software to address emerging challenges in scientific computing and machine learning. The research will emphasize both theoretical
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activities. Qualifications: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related field. Strong background in machine learning, particularly deep learning and natural
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internationally oriented institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development
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(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
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skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban Health Principles: Familiarity with urban planning principles centered on health (active mobility
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devices into complex digital systems. Advanced expertise in machine learning and artificial intelligence for predictive and prescriptive urban data analysis. Experience in visualizing and analyzing spatial
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at conferences, and stakeholder engagement sessions. Required Qualifications: A Ph.D. in Climate Science, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in