65 machine-learning-"https:"-"https:"-"https:"-"Computer-Vision-Center" Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
. In this role, you will collaborate with leading experts in autism research, computer vision, machine learning, psychiatry, and developmental science to develop innovative technologies that improve
-
affiliations. Be You. Be Bold. Choose Duke. Be You. At Duke, we celebrate individuality and the unique perspectives that each member of our community brings. As the Machine Learning Research Data Analyst
-
development, implementation, and evaluation of machine learning, computer vision, and other algorithms, primarily in the context of health. They will be part of the thriving research community of Duke
-
dissemination activities. Stay current with emerging technologies and best practices in machine learning, software engineering, and medical imaging. What You'll Bring: Ph.D. in Biomedical Engineering, Computer
-
development and application of advanced computed tomography methods, with a focus on photon-counting CT, quantitative image analysis, and machine learning. The position will involve work across
-
engineering, machine learning, and translational biomedical science. Develop modular software tools, simulation frameworks, and machine learning pipelines to support biomarker discovery and validation
-
clinical settings. The postdoctoral associate will contribute to methodological development and applied research in machine learning, statistical modeling, natural-language processing, epidemiology
-
imaging and patient care. You will join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to
-
experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Choose Duke. Duke University is
-
required. Preferred Qualifications: Experience with large-scale or high-dimensional datasets (e.g., cohort, registry, or omics data) Familiarity with causal inference methods or machine learning