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. Perform any other duties relevant to the research programme. Job Requirements: PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive
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by using explainable AI To develop generative AI techniques to design novel biologics for cancer Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or
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, Safety and Health Regulations. • Assist in project resource requirements and managing student projects. Job Requirements: • Have a PhD Degree in Computer Engineering/Computer Science/Electrical Engineering
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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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for this position: PhD in Mechanical Engineering, Structural Engineering, Civil Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation
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machine control for smart manufacturing as well as publications in top-tier international conferences and journals, as well as real-world implementations. If interested, please apply with your resume
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, leveraging advanced learning analytics, machine learning, and deep learning techniques. The candidate shall work under the supervision of the Principal Investigator (PI) and Co-PIs to conduct academic research
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Requirements: PhD degree in Artificial Intelligence, Computer Science, or a related field from a prestigious institution. Good written and oral communication skills. Proficiency in developing deep learning