296 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" Fellowship positions in Singapore
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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 opportunity
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. Learn more about our lab at https://www.richardshelab.com/ . Key Responsibilities: Design and lead a novel research project aligned with your scientific interests and the lab’s experimental strengths
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, Bioinformatics, Computational Biology, or other AI-related disciplines. Strong foundation in AI, statistical modeling, machine learning, or high-dimensional data analysis. Proficiency in programming languages
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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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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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translate this understanding into disease alleviation and prevention. For more details, please view https://www.ntu.edu.sg/medicine/research/research-programmes/nutrition-metabolism-health We are looking
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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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Research Fellow / Associate Research Fellow / Senior Analyst / Research Analyst (Military Studies Programme) The S. Rajaratnam School of International Studies (RSIS), a Graduate School of Nanyang
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research. We are looking for a Research Fellow to work on the thermal catalysis for CO2
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international collaborators across clinical, academic, and industry settings to develop privacy-preserving machine learning approaches, federated learning frameworks, and interpretable algorithms for multimodal