-
perioperative medical education. We are dedicated to developing leaders in anesthesiology and perioperative medicine who demonstrate clinical excellence, professionalism, and a commitment to life-long learning
-
is committed to diversity and equality in education and employment. Qualifications: Minimum Qualifications: PhD in computational biology, machine learning, cancer biology. Four years of experience with
-
, Machine learning, Biomedical Informatics or related fields Preferred Qualifications: Experience in developing and training NLP/deep learning models on GPUs (with framework such as PyTorch, tensorflow
-
sciences, data science, machine learning, statistics, mathematics, computer science, or related fields. Preferred Qualifications: Experience in at least one programming language such as Python or R
-
life-long learning. Our faculty have a resolve to create an exciting academic environment that will build upon what is already recognized as one of the finest residency programs in the country, and by
-
, the selected candidate will work on projects funded by the National Institutes of Health (NIH) that aim to develop image computing and machine learning methods, together with translational tools, to understand
-
limited to: Collect data from a range of in vivo physiological and behavioral studies predominantly in genetic mouse models, learn a variety of surgeries, learn gold standard methodologies to study glucose
-
meetings, present at research meetings, journal clubs, lab meetings (10%) Teach/supervise students, professional research assistants, and other lab members (10%) Developing grant-writing skills by applying
-
. The postdoctoral position will be awarded on a competitive basis to an individual who has completed a doctoral degree program (MD, PhD, DrPH, ScD, DNP, DO, or equivalent) and has demonstrated commitment and interest
-
– Requisition #:30387 Job Summary: The Pividori Lab (pivlab.org; PI: Dr. Milton Pividori) designs and implements machine-learning methods that are applied to human disease with a systems biology perspective. Our