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. Apply statistical modelling, machine learning, and deep learning approaches for biomarker discovery, disease stratification, prognostic modelling, and causal inference. Work closely with clinician
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should have experience in real-time processing or FPGA-based prototyping or embedded sensing architectures, or machine-learning-driven analysis for photon-limited measurements. Exposure to event-driven
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analytics and re-examine the role of man-machine symbiosis at all levels of framework.
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and data pipelines to enable real-time data acquisition and closed-loop control. Collaborate with AI researchers to implement machine learning models for adaptive experimental design and autonomous
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. The candidate will take the lead on machine learning and computational analyses, primarily supporting our translational research program focused on developing AI-based models to predict cognitive decline. This
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enthusiasm and interest in flexible electronics, bioelectronics, neurotechnology, brain-machine-interface research. Biological systems are typically soft and highly dynamic with cellular growth and functional
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should demonstrate strong expertise in robotic systems, with experience in one or more of the following areas: robot perception, 3D machine vision, object pose estimation/tracking, vision-guided
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Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. This is a senior scientific role
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powder bed fusion system, focusing on automation and control aspects of this novel machine and the implementation of an in-process monitoring system. This role is central to our efforts to push the
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at least a PhD Degree in Epidemiology, Public Health, Environmental Health, Biological Sciences, Biostatistics, Data Science, preferably with relevant experience. Prior experience with machine learning is a