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). Applying advanced statistical and machine learning methods (e.g., predictive modelling, clustering, multivariate integration) to large-scale time series and sensor datasets. Contributing to the development
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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to positive change in society You can expect numerous benefits , such as public transport season tickets and car sharing, a wide range of sports offered by the ASVZ , childcare and attractive pension benefits
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. Learn more about the Department of Health Policy and Management here: https://www.hsph.harvard.edu/health-policy-and-management/ Job-Specific Responsibilities: The successful candidate will work closely
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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machine learning with single-cell genomics, spatial omics, and systems biology, supported by strong collaborations across UBC and internationally. Project Recent advances in single-cell and spatial omics
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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systems. Combination of behavior with large-scale neural recordings using silicon probes, miniscope, or 2P imaging. Ability to explore and analyze large datasets using modern machine learning methods and a
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine