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Field
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partners, along with the databases we create. The geospatial smoke tradeoffs dataset can then be used in project planning. Learning Objectives: The fellow will develop professionally while engaging in
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use of molecular assays such as real-time PCR and next-generation sequencing to study the prevalence of infectious diseases in the target study populations. Learning about sample testing under a
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at Northeastern. (10%) Required Qualifications: - Ph.D. in Human-Computer Interaction, Information Science, Computer Science, Design, or related fields - Strong record of published research in HCI, CSCW, DIS
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devices, and edge computing platforms. Deploy machine learning models to enhance process control and system responsiveness. Collaborate with industry partners to identify research challenges, co-develop
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stimulating community. Fellows teach one small seminar during their fellowship year appropriate for graduate students and advanced undergraduates. Though courses are designed to fit the focal theme
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year postdoctoral position. Using recent
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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work that underpins the scientific research of the collaboration. Research Title: Process Modeling using Physically Informed Machine Learning The work will entail: § Designing and training physics
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and