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Postdoctoral Research Associate
University of Connecticut in ,
 
 
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Date Posted 05/15/2021
Category
Admin-Laboratory and Research
Employment Type
Application Deadline Open until filled
 
 
 
 
 

JOB SUMMARY
The University of Connecticut seeks a postdoctoral research associate, starting summer 2021, to collaborate on an interdisciplinary NSF-funded study. Project EMSTAR ("Ecological Modeling of STEM Teacher Attrition & Retention") will use large datasets to investigate personal, workplace, and community factors associated with science and math teacher departures/retention from their employing school. With a focus on reducing disparities imposed on high-need school systems associated with teacher turnover, outcomes will translate to empirical studies published in national journals. This project leverages a strong positive relationship with the state and has the potential of informing policy decisions. We value applicants interested in contributing to these efforts and who view such work as foundational to launching a productive research career. This project is a direct outgrowth of the project staff's commitments to educational equity and would benefit from the presence of a postdoctoral researcher similarly concerned about reducing educational disparities.
DUTIES AND RESPONSIBILITIES
Suitable candidates will have experience applying multivariate statistics, hierarchical models (Bayesian or frequentist), and survival analyses to large data sets. In addition to joining an especially amicable research team, the project's budget provides arch a competitive salary, fringe benefits, technology, and conference travel. Teaching opportunities may become available.
MINIMUM QUALIFICATIONS
  • PhD in Applied Math, Data Science, Quantitative Biology, Quantitative Methods in the Social Science, etc. Candidates with PhDs in Education will be considered with evidence of substantial quantitative/statistical training.
  • Familiarity with hierarchical models and multivariate statistics.
  • Demonstrated ability to code in R, Julia, Stan, JAGS or another programming language.
  • Excellent communication skills include experience reviewing manuscripts and writing for publication.
PREFERRED QUALIFICATIONS
  • Expertise building and analyzing mathematical or statistical models of biological or social systems
  • Facility communicating statistical analyses (via visualizations and white papers) to diverse and non-specialist audiences
  • Familiarity with public education and committed to enhancing diversity, equity and inclusive education.
APPOINTMENT TERMS
This position is a 12-month contract with potential one-year extension, based on satisfactory performance.
TO APPLY
Please apply online at https://hr.uconn.edu/jobs, Staff Positions, Search #495182 to upload a resume, cover letter, and contact information for three (3) professional references. Please direct inquiries to  john.settlage@uconn.edu.
Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check. 
This job posting is scheduled to be removed at 11:55 p.m. Eastern time on June 4, 2021. 
All employees are subject to adherence to the State Code of Ethics which may be found at http://www.ct.gov/ethics/site/default.asp.
The University of Connecticut is committed to building and supporting a multicultural and diverse community of students, faculty and staff. The diversity of students, faculty and staff continues to increase, as does the number of honors students, valedictorians and salutatorians who consistently make UConn their top choice. More than 100 research centers and institutes serve the University's teaching, research, diversity, and outreach missions, leading to UConn's ranking as one of the nation's top research universities. UConn's faculty and staff are the critical link to fostering and expanding our vibrant, multicultural and diverse University community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities and members of traditionally underrepresented populations.


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Internal Number: 495182-2276_1620641196
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