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to characterize the brain network interactions using connectivity and graph theory metrics. Develop reproducible computational pipelines in MATLAB, Python, R, or similar platforms for electrophysiology processing
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. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical analysis and data management
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Learning: Apply shape-correspondence and machine-learning approaches, including spectral/graph-based surface alignment and autoencoder-based shape extraction, to compare limb and joint morphology across
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-performance computing, bioinformatics or evolutionary theory. Required skills are; commitment to ethical conduct of research, self-motivation and independence, good documentation habits and a track record of
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Knowledge, Skills, and Abilities Doctoral degree in epidemiology, public health, mathematics, behavioral science, health services research, economics, or a related discipline that provides training and skills
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degree in epidemiology, public health, mathematics, behavioral science, health services research, economics, or a related discipline that provides training and skills in quantitative methods. Must have
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Qualifications Familiarity with designing experiments informed by commercial marketing and health communication theories. Familiarity with the study of cannabis and tobacco marketing influence and commercial
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, are also encouraged to apply. The successful applicant should have: · A Ph.D. in Earth and planetary sciences, geophysics, astrophysics, applied mathematics, or a closely related field by the start date
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., MATLAB, Python, R) preferred. Solid background in signal processing, statistics, or computational neuroscience, preferred. Outstanding presentation skills, preferred. Experience in graphing, statistical
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implementation science theories and methods. Excellent interpersonal, verbal, written communication, and organizational skills. Experience with institutional review boards (IRB). Preferred Qualifications Prior