49 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" Fellowship research jobs in Singapore
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third largest university by intake in Singapore. SIT’s mission is to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society
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accelerator design, verification, and physical implementation using open-source tools. Explore architecture-algorithm co-design for machine learning and AI acceleration. Perform performance, power, and area
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work environment Learn more about CQT at https://www.cqt.sg/ Job Description A postdoctoral research fellow position on the topic of quantum error correction is available in the group of Associate
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independently and as part of a team Experience with machine learning and AI applications in engineering is advantageous We regret to inform that only shortlisted candidates will be notified. Hiring Institution
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Intelligence, or a closely related discipline. Strong research background in AI and machine learning, with a focus on efficient or accelerated models. Proven experience with model compression techniques, such as
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staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the
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third largest university by intake in Singapore. SIT’s mission is to innovate with industry, through an integrated applied learning and research approach, so as to contribute to the economy and society
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work environment Learn more about CQT at https://www.cqt.sg/ Job Description We have openings for talented early-career scientists who are ready to take up a leadership role in our group and to spearhead
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superlattices (twistronics). The role will focus on developing and applying theoretical models and computational quantum chemistry and machine learning methods to uncover novel properties and phenomena in low
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly