Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
? As an Oak Ridge Institute for Science and Education (ORISE) participant, you will join a community of scientists and researchers studying adversarial interactions, multi-agent systems, and decision
-
creates a pathway for you to advance your PhD thesis research while conducting research at a DOE National Laboratory, collaborating with world-class scientists, and using state-of-the-art facilities and
-
experience in animal and/or veterinary science to perform live animal studies and subsequent evaluations of swine viral disease pathology, preventions, and/or treatments. Experiments will be performed with
-
you submit your application. Your recommenders will receive an email with a subject line of "[Your Name] - ORISE Recommendation Request - [your email]", from [email protected] . This email will
-
experience in animal and/or veterinary science to perform live animal studies and subsequent evaluations of swine viral disease pathology, preventions, and/or treatments. Experiments will be performed with
-
), Chronic Wasting Disease (CWD), Scrapie, and emerging camel prion diseases. You will receive hands-on training and mentorship while contributing to high-impact studies that combine cell-based systems
-
expand your research skills and knowledge under the mentorship of PhD-level scientists in entomological-based research, experimental design, and data analysis. Mentor(s): The mentor for this opportunity is
-
, psychology, and immunology. For more information, visit us on Facebook at www.facebook.com/USAMRICD . USAMRICD is offering this research opportunity for current or recently graduated BS, MS or PhD candidates
-
complete list of academic fields under Discipline(s) below. The PhD must have been conferred within the previous 60 months and must have included an emphasis on structural components and/or structural
-
/abstract and full-text screening using established software platforms, and extract relevant clinical data from selected studies. Assessing the risk of bias and methodological quality of included studies