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Field
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Learning. Experience with the deep learning ecosystem and high-performance computing infrastructures. Experience designing and conducting experiments with human participants is a strong plus. Experience in
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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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, the Postdoctoral Researcher will drive research at the intersection of health data science, multimodal AI, digital twins, computational phenotyping, and responsible AI. PhD must have been received within the last
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neurocritical care research The Opportunity We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis
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Science, or related field Knowledge and experience in computer vision, machine learning, and deep learning Good written and oral communication skills Experience in leading research projects Proficiency in basics
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on research projects spanning evaluation of deep learning neural networks trained on signed language recognition. The fellow will work closely with the PI Annemarie Kocab and collaborator Alex Lu , Senior
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school go to the following link - https://www.unsw.edu.au/engineering/our-schools/computer-science-and-engineering Skills and Experience PhD (or soon to be awarded) in computer science and artificial
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of Applied Learning, the Singapore Institute of Technology (SIT) works closely with industry partners to deliver translational, impact-driven research. This role supports applied food processing
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within the digital twin environment Developing deep learning architectures for time-series forecasting, anomaly detection, and predictive maintenance of the physical asset Designing and training Physics
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs