-
for analysis utilizing quantitative data collected from prior trials of prevention programs and frameworks like PBIS, Double Check, Coping Power, MTSS-B, and the R-CITY model. Lead and co-author papers
-
learning, small data learning · Active learning, Bayesian deep learning, uncertainty quantification · Graph neural networks This position involves active participation in a well-funded
-
metabolism in immune cells and applies these concepts to autoimmune diseases, inflammation, and cancer. More information about the Voss Lab can be found at: https://wzs4ya.wixsite.com/voss-lab . The focus
-
of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
-
the scientific component of this posting, please contact Dr. Marieta Pehlivanova via email at mp8ce@uvahealth.org . This is an Exempt level, benefited position. For more information on the benefits at UVA, visit
-
position is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding. Minimum Qualifications: Education: PhD in the Biosciences or an MD
-
research using complex observational healthcare data, with a focus on cancer studies. The successful candidate will be expected to: Modeling multilevel survival data while addressing confounding and missing