Postdoctoral Diversity and Innovation Scholars Program (University of New Hampshire) – Research[...]

The International Society for Bayesian Analysis; Durham (NH); On-site; USD 55,000 - 75,000; Full time

Benefits offered by this job

Professional development support

Participation in faculty development academies

Coaching by senior faculty

Job summary

A leading academic institution is seeking a Postdoctoral Research Associate to join the Aktekin-Glynn group in their Peter T. Paul College of Business and Economics. The fellow will conduct significant research in Bayesian statistics and its applications in business analytics, with an opportunity for faculty development and teaching.

Qualifications

• Strong background in Bayesian statistics and analysis.
• Experience with statistical programming environments like R and Python.
• Capable of conducting high-impact academic research.

Responsibilities

• Conduct high-impact research in Bayesian methods and business analytics.
• Develop statistical forecasting systems adapting to demand changes.
• Teach one course annually as part of the role.

Skills

Bayesian statistics

Statistical programming (R and/or Python)

Graphical models

Time series analysis

Education

PhD in Statistics, Data Science, or related field

Job description

Postdoctoral Diversity and Innovation Scholars Program (University of New Hampshire) – Research Associate in Bayesian Methods

Postdoctoral Diversity and Innovation Scholars at the University of New Hampshire
Peter T. Paul College of Business and Economics
Department: Decision Sciences

Description: Applications are invited for a postdoctoral fellowship in the research group of Tevfik Aktekin and Chris Glynn at the Peter T. Paul College of Business and Economics at the University of New Hampshire. The Aktekin‐Glynn group focuses on statistical methods and fast estimation algorithms to improve decision‐making in dynamic and uncertain environments currently focusing on sequential Bayesian analysis of high‐frequency count data. The postdoctoral fellow will conduct high‐impact academic research at the intersection of Bayesian statistics, business analytics, and high‐frequency demand forecasting. In collaboration with mentors Aktekin and Glynn, the postdoctoral fellow will work to develop statistical forecasting systems that (i) frequently update, (ii) borrow information across time and geographic regions, and (iii) flexibly adapt to structural changes in customer demand. The ideal candidate will have a strong background in Bayesian statistics, graphical models, and time series analysis.

Experience with a statistical programming environment (R and/or Python) is required. Additional professional development support will be provided in the form of participation in faculty development academies with early career tenure track faculty, coaching by senior faculty colleagues, workshops on teaching and learning, involvement in department activities, and support funds. Responsibilities will include teaching one course annually.