278 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "The Institute for Data" Fellowship positions in Singapore
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to groom the next generation of leaders, thinkers, 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
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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
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offers a friendly and international work environment Learn more about CQT at https://www.cqt.sg/ Job Description We are looking for a Research Fellow (RF) to join the team and explore the physics
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position within a Research Infrastructure? No Offer Description As a University of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry in its research pursuits
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about the Singapore English Mental Lexicon project is available at https://langcomplab.github.io/singlish.html Information about the department is available at: https://fass.nus.edu.sg/psy
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progress. Ability and willingness to work some flexible hours. Extensive experience in large-scale pre-training of large language model. Experienced in developing machine learning algorithms and large
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and therapy of cancer (radiotheranostics), multi-modal molecular imaging and nanotheranostics (various forms of nanoformulas). More information on the research work is available at https
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sustainability. For more details, please view https://www.ntu.edu.sg/ase . The Wetland Carbon Lab at NTU’s Asian School of the Environment (ASE) focuses on understanding the role of aquatic ecosystems, including
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modelling and machine learning for large and complex datasets. Have proficiency in Python and/or R for time-series and sensor data analysis. Have an interest in or experience in environmental exposure
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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