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Processing and Machine Learning to develop signal processing and machine learning algorithms and methods for communication networks. Key Responsibilities: Develop signal processing and machine learning
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developing AI-based smart healthcare monitoring devices, focusing on biosensor development, lab-on-chip, and point-of-care technologies. Apply machine learning and computer vision techniques to analyze
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, tissue and organ mechanics/dynamics and mechano-signal transduction. The institute operates on a fully integrated open-lab philosophy, with an extensive infrastructure supported by core facilities
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. An understanding of Singapore's economic system and local work experience is desirable. Job Description The specific area of work will depend on the priorities of the institute at any given point in time. In
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Singapore. Key Responsibilities: Collect data and signal processing Assist both lab and field tests Publish papers in top journals Write proposals for further development Mentor students Job Requirements: PhD
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knowledge in signal representation/processing, esp for scent signals. Prior research experience and track record in signal detection, machine learning and deep learning. Prior programming experience in state
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background. The cohort has completed recruitment of >80,000 participants at the point of this posting. With comprehensive behavioral, phenotypic, and multi-omic data, and electronic health record linkage
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of diverse ethnic background. The cohort has completed recruitment of >80,000 participants at the point of this posting. With comprehensive behavioral, phenotypic, and multi-omic data, and electronic health
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wearable devices using digital knitting and advanced fabrication techniques. Motion Sensing & Data Processing: Implement and optimize algorithms for joint motion tracking, integrating signal processing and
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. Experience in data acquisition, signal processing, and computational tools. Demonstrated ability to publish in peer-reviewed journals. Strong communication and teamwork skills. Experience in project management