Sort by
Refine Your Search
-
restricted data, developing and implementing statistical models, preparing requests for results disclosure, and contributing to peer-reviewed manuscripts. Project #2: The Research Scientist will also
-
their own independent statistical methods research program, engage in collaborative research with researchers across the health sciences and other areas of the University of Minnesota, seek external funding
-
regard to other projects. • Perform statistical analyses for projects led by other team members • Mentoring lab student workers 15%: You will also be expected to assist with research grants submitted by Dr
-
Twin Cities invites applications for a postdoctoral position. Candidates are expected to have received a Ph.D. in Mathematics, Statistics, or a closely related field by the start of the appointment
-
into the department’s records management system or other databases; identifying and maintaining criminal activity data, known offender and statistical information, and performing a variety of related duties associated
-
similar scope as assigned. Qualifications Required Qualifications • PhD in Computer Science, Statistics, Mathematics, Physics, or Astronomy with 1 or more years of research experience/training. Preferred
-
and addiction engagement opportunities, and a natural collaborative nature. Job Duties/Responsibilities: Statistical Analysis and coding - 70% ●Data Analysis: Clean, manage, and process high-volume
-
statistics and standardize complex economic and ecological data into index-based scores for all Minnesota lakes greater than 10 acres. ● Coordinate with team members to integrate these ecosystem service values
-
experience to equal at least eight years Experience working with IPUMS data 2+ years of experience working with statistical software package Excellent written, verbal, and interpersonal communication skills
-
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