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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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Science and Artificial Intelligence, with a focus on visual reasoning and robotic systems. The Research Fellow position involves developing novel approaches that integrate computer vision, natural
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processing and statistical machine learning techniques to mine self-reports and sensor data to gain new insights towards assessment and longitudinal monitoring of bipolar disorder; Work on sleep datasets
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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of the following: cancer biology / cancer immunology / epithelial cell biology / evolutionary biology / genomics / statistics / mathematics or machine learning* Technical qualifications: For bench
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on advancing AI technologies for space systems with significant national and global impact. Visit the AU website to learn more about the School of Computer and Information Technology . Our people Our people
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-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and
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that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes vulnerable groups
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical
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the Department). The position is financed by the University of Bergen. About the project/work tasks The PhD project aims to investigate how methods from Scientific Machine Learning (SciML) can enhance modelling