75 assistant-professor-and-data-visualization Postdoctoral positions at Duke University
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. The Postdoctoral Associate will apply his/her technical skills toward development and implementation of machine learning, computer vision, and other algorithms for analysis of medical images and prognostication as
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experimental procedures. Data preparation for oral presentations, grant applications and publication in professional journals. Implementation of bioinformatics approaches. Conformance to standards of responsible
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, including literature review, experimental design, data analysis, collaboration, and dissemination of findings through conferences and publications. Apply for fellowships and awards, and provide mentorship
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Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer programming, preferably python, and will ideally have experience in working with
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27710, United States of America [map ] Subject Areas: Statistics / Statistics Biostatistics / Biostatistics and Data Science Data Science / Machine Learning Appl Deadline: none (posted 2025/02/12
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for a full time academic or research career. Duties will include curating imaging and outcomes data, managing research projects and workflow, working with statisticians to analyze data, as
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will involve analyzing patient data and coordinating analysis of patient samples. In addition to a copy of their resume, applicants are encouraged to submit a cover letter detailing their interests and
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identity, genetic information, national origin, race, religion, sex, sexual orientation, or veteran status. Duke aspires to create a community built on collaboration, innovation, creativity, and belonging
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, gender, gender expression, gender identity, genetic information, national origin, race, religion, sex (including pregnancy and pregnancy related conditions), sexual orientation or military status. Duke
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opportunity to develop innovative statistical methods in clinical trial design and variable selection methods in high dimensional data that will predict clinical outcomes and meta-analyses. The successful