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diffusion models using path integral formulations. This project aims to advance quantum machine learning by: Designing a quantum counterpart of diffusion models; Leveraging path integral methods to model
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Investigator (PI) or team lead with project management tasks. Job Requirements: PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace. Evidence of developing Machine Learning and
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team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
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team to conduct the research on development of AI models and algorithms for image processing, computational imaging as well as computer vision applications. The roles of this position include: Research
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, electrical & electronic engineering, or equivalent. Background knowledge in signal representation/processing, visual data compression, and data-driven and machine learning/analysis. Prior research experience
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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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. The role will focus on developing machine learning and mathematical optimization solutions for electric vehicle fleet charging optimization under different constraints. Key Responsibilities: Formulate
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: Python, R, Java, C++, C#.Net, SQL. Proficiency in using Business Intelligence tools (e.g. Power BI, Tableau or Qlik Sense) Experience in applying machine learning techniques and designing algorithms
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, reinforcement learning, AI agents, and machine learning. Job Description Conduct research in the field of neural networks, natural language processing (NLP), and large language models (LLMs). Independently read
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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