Sort by
Refine Your Search
-
Partner with experts in child development, psychiatry, engineering, computer vision, and machine learning. Contribute to study design, scientific strategy, and interdisciplinary research planning. Work
-
research in engineering and science related to critical minerals. This position will contribute to building interdisciplinary excellence and research pathways in critical minerals research at Duke University
-
. The Department of Neurobiology is looking for a highly motivated Postdoctoral Associate to join our team investigating the neural mechanisms of motor learning and long-term motor memory. In this role, you will
-
engineering, and data science, you will contribute to high-impact translational studies integrated within innovative clinical trials. This position is fully grant funded and offers exceptional opportunities
-
development of machine learning tools and their applications to medical imaging. Key Responsibilities: The Post Doctoral Associate will apply their technical skills toward the development, implementation, and
-
multidisciplinary team members and the broader scientific community. Contribute to a culture of innovation, collaboration, and continuous learning. Support Research Excellence Adhere to all institutional requirements
-
neurologists, neurosurgeons, sleep medicine specialists, engineers, and data scientists, you will have the opportunity to contribute to groundbreaking discoveries while expanding your scientific expertise in a
-
therapy, with a particular emphasis on epigenetic regulation, radiation response, and treatment resistance. Our work leverages patient-derived organoids, engineered cellular models, functional genomics, and
-
-edge technologies, including genetically engineered mouse models, patient-derived models, single-cell and spatial genomics, organoid systems, and preclinical therapeutic studies. Learn more about our
-
material damage assessment 3) Developing AI and machine learning models for robot-assisted laser surgery and validate the model by comparing the results to experimental observations. 4) Support the