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We invite excellent candidates in classical machine learning to join a collaborative research initiative between the MathEXLab of NUS (Mechanical Engineering) and the Centre for Quantum Technologies
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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning
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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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mentorship. Develop, implement, train, and validate machine learning and deep learning models for AI-driven prediction of cell physiology, metabolism, and functional behavior, with input from the research team
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goals will also be explored during the later stages of the selection process. You should also have: solid knowledge of artificial intelligence and machine learning techniques, including their limitations
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of unknown PFAS, supporting agricultural research and advancing the ARS mission. The project will involve compiling high-resolution mass spectrometry (HRMS) databases for PFAS, creating machine learning models
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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a machine learning model for storm surge prediction. Key Responsibilities Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure
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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced