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in numerical analysis, partial differential equations (PDEs), and scientific computing. Solid background in machine learning theories, with specific experience in Physics-Informed Machine Learning
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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requirements PhD in Physics, Applied Mathematics, Computational Science, or a related field Strong background in machine learning, particularly in the development and application of neural networks
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: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering, or equivalent. Independent, highly analytical, proactive, and a team player; strong verbal and written communication skills
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frameworks for advanced property prediction and analysis of inorganic disordered materials. Carry out machine-learning based first-principle calculations aimed at advancing the understanding defect-based
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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research grants in the above areas Job Requirements: A PhD degree in Computer Science, Data Science, Engineering, or a related field. Research experience in Computer Vision, Image Processing, Multimedia
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, including protein structural analysis Job Requirements: PhD in Biological Sciences, Bioinformatics, Computational Biology, or related fields Prior research experience in machine learning or systems biology is
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operations, such as data storage, budgets, expenses, assets, and ethics approvals. Key Responsibilities: Conduct independent and collaborative research applying AI and machine learning techniques