127 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" positions in Singapore
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Research Assistant to carry
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for drone swarms. The role will focus on multi-agent visual perception techniques. Group website: https://personal.ntu.edu.sg/wptay/ Key Responsibilities: Develop signal processing and machine learning
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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Job related to 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
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-driven Design Automation for Advanced Packaging to develop and integrate artificial intelligence methodologies into advanced packaging electrical design. The role will focus on leveraging machine learning
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at its fingertips by enhancing the use of effective applications and services for teaching and learning. We drive a culture that is forward-looking. With a strong passion for IT, our people are always
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and machine learning. Knowledge of the basics of federated learning and causal inference is highly encouraged. Proven track record in research and development of machine learning algorithms. Proficiency
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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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the end-to-end discovery process, including data preparation, statistical analysis, and methods for extracting meaningful insights from large datasets. Core machine learning techniques such as decision tree
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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