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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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, 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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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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handling stations. · Evaluate formulations in cell culture and animal models. · Collaborate with our team to conceive, implement, and test novel computational methods for drug carrier design
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the vagal complex with multiple imaging modalities, spanning gross anatomy to nerve morphology to microscopy resolving individual neurons. These imaging data provide inputs to computational models
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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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tools, high-throughput screening, and human induced pluripotent stem cells (hiPSCs) to model different cell types, phenotypes, and disorders. We are looking for a highly motivated and talented candidate
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to advance the application of computational hemodynamic models of cardiovascular flow to aid in remote tracking and early diagnosis of disease. The applicant will gain exposure to other projects in the lab
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developing and applying advanced statistical models, machine learning, and deep learning approaches. As such, we seek applicants with strong quantitative backgrounds in remote sensing and time series analysis