73 big-data-and-machine-learning-phd "https:" Postdoctoral positions at Duke University
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
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
-
. 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
-
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
-
with experience or interest in machine learning, statistical modeling, natural-language processing, clinical or population-health data, reproducible research, and interdisciplinary collaboration
-
, machine learning, or biomedical data science. Strong programming and software development skills. Experience working with complex biomedical, imaging, or clinical research datasets. Excellent scientific
-
program focused on development of machine learning tools and their applications to medical imaging. Key Responsibilities: The Post Doctoral Associate will apply their technical skills toward the
-
Care and Hospice, Duke Health and Wellness, and multiple affiliations. Be You. Be Bold. Choose Duke. Postdoctoral Associate, Diagnostic Imaging & Machine Learning Be You. At Duke, we celebrate
-
to apply and extend machine learning and data-driven methods, particularly in settings that require combining biological, clinical, and social determinants of health. The position emphasizes
-
with hierarchical modeling and mixed effects models. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae
-
morphometry Machine learning and deep learning for CT image analysis and reconstruction Computational and digital-twin models for preclinical imaging Analysis of longitudinal and dynamic imaging data