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Requirements: PhD or master’s degree in computer science, Computer Engineering, Computational Social Science or its equivalent. Bachelor’s degree holders need to have at least three years of experience working
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the system Development of inverse design frameworks using machine learning Development of full simulation for the chip-scale chirped-pulse amplification Use the full simulation to guide system fabrication
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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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: 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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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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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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science, machine learning, artificial intelligence, or a related field. Candidates with a PhD may be considered for a Research Fellow position instead. Prior experience with video data visualization research will be
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Responsibilities: Conduct research in the domain of real-time scheduling and resource allocation problems for machine learning pipelines deployed in safety-critical cyber-physical systems. Close collaboration with
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. Job Requirements: PhD degree in Computer Science, Computer & Electronics Engineering or other related fields Strong background and knowledge in Cybersecurity, especially network security and cellular
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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