-
join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
-
You'll Bring Required Qualifications PhD or equivalent doctoral degree (ScD, MD, DVM, or related terminal degree). Strong background in one or more of the following: Computer Vision Machine Learning
-
. The Di Talia lab at Duke University School of Medicine seeks a highly motivated postdoctoral research fellow for a fully funded position in our lab. Minimum Requirements: PhD in a Cell Biology
-
discoveries made in the laboratory with meaningful improvements in patient care. You will be part of a research environment that values curiosity, creativity, scientific rigor, and continuous learning while
-
. The successful applicant will possess a PhD or equivalent doctoral degree in the social sciences, including political science, public policy, political ecology, law, geography or another relevant field with some
-
Academic credentials: PhD in ecology, biology, statistics, computer science or engineering, or oceanography with a strong quantitative analysis background, particularly in species distribution modeling
-
. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
-
meetings, contribute to grant applications, and build an independent research career in cancer immunology and neuro-oncology. Minimum Requirements PhD, MD, MD/PhD, or equivalent doctoral degree in immunology
-
, measurement, and evaluation strategy, with a focus on cross-sector data infrastructure, linkage, performance measurement, evaluation, and continuous learning. Responsibilities: 1. Serve as a scientific partner
-
. or equivalent doctorate (e.g. Sc.D., M.D., D.V.M.) Preferred Qualifications:. A PhD or MD/PhD (or equivalent) in biological sciences Strong research background in cell biology, molecular biology, mouse models