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Field
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Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students
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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea
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, wearable physiological sensing, and machine learning to uncover how factors like fatigue and cognitive workload impact technician performance. Join us to develop predictive models that predict human error
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new genomic tools and predictive models to understand ecosystem resilience, climate change impacts, and environmental health. In addition to advancing fundamental science, the initiative seeks
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of NeoShield's two core machine-learning systems: a Clinical Decision Support Tool for neonatal sepsis diagnosis (predicting likelihood of infection and informing antibiotic decision-making), and a real-time ward
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. The programme will explore the biodiversity and biological adaptations of plants, animals and microorganisms to extreme mountain environments, while developing new genomic tools and predictive models
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other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases
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of NeoShield's two core machine-learning systems: a Clinical Decision Support Tool for neonatal sepsis diagnosis (predicting likelihood of infection and informing antibiotic decision-making), and a real-time ward
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(SingFarm)—a CREATE-NRF programme focused on building a sustainable, data-driven food production model with strong industry relevance to contribute Singapore’s ambition in achieving 20 per cent local
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predictive models for drug response. Furthermore, we work on creating new treatment stratification methods to personalize treatment for individual patients. More information about the lab can be found here