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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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, BioRad BioPlex, Phadia250) Automated IFA and/or ELISA processors (e.g., BioRad PhD). Electrophoresis apparatus and scanners (e.g., Sebia Phoresis, Assist, Hydrasys) Immunofluorescent microscopes (Nikon and
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statistical and machine learning methodologies to analyze and predict aspects of the collected data With the guidance of Drs. Stuber and Bruchas, develop experimental methodologies related to two-photon imaging
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, using Big Data and machine learning. The Research Scientist / Engineer 2 (RS/E 2) will play a critical role in these important studies. This is a tremendous opportunity to grow as part of a very
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, using Big Data and machine learning. The Temporary Research Scientist / Engineer 3 (RS/E 3) will play a critical role in these important studies. This is a tremendous opportunity to grow as part of a
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Job Description OVERVIEW: The Computational Ophthalmology Lab is leading collaborative efforts on large multidisciplinary research projects, using Big Data and machine learning. The Research
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, BioRad BioPlex, Phadia250) Automated IFA and/or ELISA processors (e.g., BioRad PhD). Electrophoresis apparatus and scanners (e.g., Sebia Phoresis, Assist, Hydrasys) Immunofluorescent microscopes (Nikon and
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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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the design, deployment and evaluation of advanced analytics, machine learning, and related generative AI capabilities that enhance clinical and operational decision-making, population health management