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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 15 hours ago
experience and interest in mental health, suicide prevention, health professions training, and/or military health, as demonstrated through any or all of the following: A record of scholarly publication and
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The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model that can
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neuroimaging, including substantial experience with EEG and/or fMRI data analysis and programming. Proficient in advanced neuroimaging data analysis, computational modeling, machine learning, and programming
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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within a Research Infrastructure? No Offer Description Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the
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of the Post-Doc candidate will be. The project will leverage system integration and optimized operation using machine learning and AI techniques as key drivers for improving system performance. The hired
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include: Item response theory, causal inference in non-experimental designs, psychometrics, randomized controlled field trials, longitudinal and multilevel modelling, machine learning methods, artificial
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systems. You will also explore the cutting-edge application of AI and machine learning in channel prediction. As an active member of CWI, you will contribute to our world-class research output by publishing
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background/interest in time-series analysis, theoretical machine learning on networks, and high-dimensional statistics. Key Responsibilities: Take the lead in developing sub-projects (problem formulation
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model