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analysis using appropriate machine learning techniques and contribute to the writing of technical papers and research proposals. Duke is an Equal Opportunity Employer committed to providing employment
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applications of deep learning, medical imaging, and biomarker integration. This full-time, one-year position offers a unique opportunity to engage in impactful research at the intersection of AI, connectomics
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policies pertaining to other schools at Duke University. The postdoc candidate is expected to: 1) Develop novel methods for incorporating scientific machine learning in solving problems in solid mechanics
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental
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the ability to work together with colleagues and teach and mentor students from diverse backgrounds and perspectives. To apply, candidates should submit a cover letter, curriculum vitae, and contact information
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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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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to mentor students, teach/train other researchers in LCA tools, and develop independent research projects as desired. The successful applicant will possess a PhD in chemical engineering, chemistry
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, generous retirement benefits, and a wide array of family-friendly and cultural programs to eligible team members. Learn more at https://hr.duke.edu/benefits/ Duke is an Equal Opportunity Employer committed