Postdoctoral Research Position in Statistics and Data Sciences
The Department of Statistics and Data Sciences at University of Texas at Austin invites
applications for up to two postdoctoral fellows to conduct research at the intersection of
Bayesian methods, causal inference, and/or spatial statistics. The position will be primarily
supervised by Dr. Cory Zigler or jointly supervised by Dr. Zigler and Dr. Fabrizia Mealli, with
opportunities to conduct research at the Department of Statistics, Computer Science, and
Applications at the University of Florence, Italy. Opportunities to collaborate with other faculty
in the Department of Statistics and Data Sciences and across the University of Texas will be
part of both positions, in particular through established relationships with the Dell Medical
Expertise in causal inference or spatial statistics (not necessarily both) is strongly encouraged,
with expectations to contribute to ongoing methodological development in causal inference
with interference, analysis of spatial network data, heterogeneity of causal treatment effects,
principal stratification, causal mediation analysis, and risk prediction.
The successful candidate will be encouraged to augment contribution to ongoing research
projects with his or her own independent research agenda.
PhD in statistics, biostatistics, ecology, environmental science, or other related field.
Experience in causal inference methodology or spatial statistics strongly preferred.
To apply, please email a cover letter describing research interests and experience
along with a CV and names of three references to: firstname.lastname@example.org.
Or apply through the UT website:
Background check conducted on applicant selected.
Equal Employment Opportunity Statement:
The University of Texas at Austin, as an equal opportunity/affirmative action employer,
complies with all applicable federal and state laws regarding nondiscrimination and affirmative
action. The University is committed to a policy of equal opportunity for all persons and does
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orientation, gender identity, gender expression, disability, religion, or veteran status in
employment, educational programs and activities, and admissions.