75 machine-learning Fellowship positions at Nanyang Technological University in Singapore
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow'. We welcome you to join our community
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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characterization. Experience or interest in conducting interdisciplinary research, particularly in the intersection of machine learning and materials informatics. Ability to work effectively in an interdisciplinary
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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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, engineering, finance, and health. Key Responsibilities: To perform the pioneer research in AI for climate transformation. To further develop data-driven and machine learning tasks for fighting climate changes
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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field of engineering Research experience in one or more of the following: geometry, optimization, dynamical systems, mechanics, probability and statistics, data science, machine learning Evidence of
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Responsibilities: To independently undertake research in data privacy, machine learning, and applied cryptography. To produce research papers and reports as required by the funding body or for dissemination
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. Publish high-quality research in top-tier conferences and journals. Job Requirement: Ph.D. in Computer Science, Robotics, or a related field. Expertise in machine learning and safe navigation algorithms
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Engineering, Industrial Engineering, Machine Learning, Artificial Intelligence, or related fields Expertise in numerical simulation of multi-physics systems, especially fluidic problems Expertise in generative