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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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parcellation (Glasser et al., 2016 Nature). The post-doc will be co-mentored by Matthew F. Glasser MD/PhD and David C. Van Essen PhD and be based in the Glasser/Van Essen laboratory in the WashU Radiology
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities
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, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment
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Position Overview School / Campus / College: College of Engineering Organization: Electrical and Computer Engineering Title: Research Assistant Professor (Non-Tenure) - Li Lab Position Details
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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood
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the Center’s co-founders and affiliate faculty, including sociologists, information scientists, computer scientists, and policy experts. Located in Seattle, the position also offers opportunities for engagement
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implement new computational and statistical methods. Create, test, and use relevant computer code (R, Python, SQL or equivalent). Maintain, modify, and execute analytic machinery that results. Draft
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analysis; Biomarker identification through the use of machine learning approaches; and Multi-omics data integration with genomics, transcriptomics and methylomics data. Job Description Primary Duties
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Qualifications: Completion of a PhD in aerospace engineering, physics, or a related field at the time of the appointment. Experience in experimental plasma physics. Ability to function and thrive in a