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). Postdoctoral Fellow, Multimodal Machine Learning Role Overview The Postdoctoral Fellow will develop techniques to build a foundation model of human neurotypes by integrating cognitive task performance
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research program in computational oncology, centred on new quantitative and machine-learning methods, including artificial intelligence and generative modelling, that integrate single-cell genomics and
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or Accountabilities: Assist with the preparation, organization, cleaning, and management of speech and language datasets. Support the development, testing, and refinement of machine learning and
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, proteomics); biomarker discovery for pregnancy complications and neonatal outcomes; computational biology, machine learning, and predictive modelling; translational studies integrating laboratory discoveries
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successful candidate will bring expertise in fire behaviour and climate modelling, remote sensing, and machine learning to a multidisciplinary research program that spans prescribed fire operations, fire
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Engineering side, research areas include Operations Research, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our
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pipelines to test measurement quality, reliability, and validity. Partner with the machine learning team to define interpretable features and target evaluations for multimodal models. Qualifications: PhD
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application of quantitative and machine learning methods to health research. The position will report to the Principal Investigators and will work closely with other members of the research team. The
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complications and neonatal outcomes; computational biology, machine learning, and predictive modelling; laboratory-based biomarker discovery and validation using maternal and neonatal biospecimens
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University of British Columbia - Faculty of Pharmaceutical Sciences | Northern British Columbia (Fort Nelson), British Columbia | Canada | 18 days ago
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