Minneapolis, Minnesota
New York, New York
United States - Nationwide
Charlottesville, Virginia
Pittsburgh, Pennsylvania
Philadelphia, Pennsylvania
Los Angeles, California
New Orleans, Louisiana
Montreal, Quebec, Canada
Ithaca, New York
Research Triangle Park, North Carolina
Rockville, Maryland
Rockville, Maryland
Posted: 16-Nov-23
Location: Minneapolis, Minnesota
Salary: 58,000.00 - 60,000.00
Internal Number: 358639
The School of Public Health (SPH) is committed to antiracism and anti-oppression and welcomes you to join us in our pursuit of building equity and driving justice. We particularly encourage applications from those who belong to groups that have been historically underrepresented in our School, including those who are American Indian, Black, Indigenous, and people of color, those with disabilities, veterans, and those from LGBTQIA+ communities.
The School of Public Health Division of Biostatistics and Health Data Science is seeking applications for a full-time Post-doctoral Associate in Biostatistics (9546 Post-doctoral Associate).
Starting pay is dependent upon the selected candidateâ™s relevant qualifications, experience, and internal equity. $58,000-$60,000.
Work Arrangements: The University of Minnesota endorses a âœWork with Flexibilityâ approach that encourages employees to select a work location where they can do their best work. We offer a flexible work environment that meets the needs of our students, faculty, staff, and partners we serve. This position will have a hybrid work option.
Duration: This appointment is for 1-2 years, with a possible extension to year 3, contingent on satisfactory performance and funding availability.
Starting Date: Negotiable. Position will remain open until filled.
The successful candidate will be working with Dr. Thierry Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would be able to systematically integrate biomedical/biological knowledge to improve the prediction power of clinical outcomes. Domains of applications may include cancer and cardiovascular diseases, and neurodevelopment disorders. More details on the study goals can be found on this link. The post-doc will also work on software development (in R, or in Python, Java, Stan, or BUGS or interfacing R with C/C++), simulation studies, real data analysis, and writing manuscripts.
Questions? For preliminary inquiries, you may send your CV to tchekouo@umn.edu.
A PhD degree in Biostatistics, Statistics, or a related field, strong computing/programming and communication skills, and a strong interest in omics and/or imaging data analysis are required. Experience in Bayesian high-dimensional data analysis is highly preferred.
