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computational and data analytical methodology development and implementation; experience in supervised and unsupervised machine learning, low-dimensional models or deep learning models, and willingness to learn
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity
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models using a wide variety of data, including clinical, wearable devices, neuroimaging, and –omics data from electronic health record, registry, and research initiatives; and (2) develop and apply novel
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on innovative projects exploring receptor-specific contributions to behavior and circuit function. Work Performed • Conduct stereotaxic surgeries and pharmacological manipulations in mouse models. • Perform
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, work will involve the evaluation and modeling of plastic fragmentation and additive release from plastics as well as life cycle assessment of materials processing. Candidates should have a doctoral
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restoration of function. The successful applicant will combine computational modeling, engineering optimization, and in vivo experiments to advance understanding and application of electrical block of neural
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recent Ph.D. in microbiology, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative
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opportunities for advancing Manoomin resilience. The postdoctoral associate will be expected to conduct field-based research to understand the governance dynamics around Manoomin. The research will involve
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Postdoctoral position at Duke University is available to conduct research on chronic pain. Using a combination of animal models, behavioral tests, imaging, patch-clamp, neuroanatomy, genetic, molecular biology