Postdoctoral Position in Biostatistical Methodology

The Department of Biostatistics and Medical Informatics at the University of Wisconsin-Madison (UW-Madison) invites applications for a post-doctoral position in biostatistical methodology under the mentorship of Dr. Lu Mao (https://biostat.wisc.edu/~lmao/). This position is open immediately until filled, with an expected appointment length of 2 years. The successful candidate will receive balanced training in both methodological and collaborative research. The training process aims to build a path for the trainee’s eventual independence and success in the academia.

This position involves primarily NIH- and NSF-funded methodological research in one or more of the following areas (aiming at publication in top-tier statistical journals):

1.      Statistical methods for clinical trials of chronic disease (topics include composite endpoints, group sequential trials, etc.)

2.      The instrumental-variable (IV) approach to randomized controlled trials with non-compliance

3.      Statistical and machine-learning methods in diagnostic medicine

For brief introductions to the funded projects, see

·         https://grantome.com/grant/NIH/R01-HL149875-01 (NIH R01HL149875);

·         https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015526 (NSF DMS-2015526).

In close alignment with the methodological study, the trainee will also have hands-on experience collaborating with empirical researchers in related biomedical fields. Potential projects include:

1.      Cancer and cardiovascular clinical trials with the Data Coordinating Center of UW-Madison led by Prof. KyungMann Kim (https://biostat.wisc.edu/research/clinical-trials/data-coordinating-center/);

2.      Development and evaluation of diagnostic tests using quantitative imaging biomarkers led by PIs from the Departments of Radiology and Medical Physics at UW-Madison.

Candidate Qualifications

The candidate must have a PhD degree in Statistics, Biostatistics, or a related quantitative field. The following qualifications are highly valued: (a) Strong background in statistical theory and methodology; (b) Proficiency/experience in R-package development; (c) Excellent writing and communication skills.