Department of Decision Sciences, George Washington University
We are looking for a highly motivated postdoctoral researcher with a strong interest in optimization. The position will involve the development of novel reformulation and algorithmic methods for stochastic, distributionally robust, and mixed-integer nonlinear optimization problems. The successful candidate will conduct research at the intersection of stochastic programming, optimization under decision-dependent uncertainty, convex optimization, and combinatorial optimization. The postdoctoral researcher will have the opportunity to develop new mathematical models, reformulation techniques, and algorithms motivated by exciting application domains, including wildfire resilience, critical infrastructure protection, network design, and emergency healthcare operations such as opioid overdose response and out-of hospital cardiac arrest systems.
We are looking for candidates with strong experience and interest in:
- Development of reformulation and algorithmic methods for mixed-integer, convex, and stochastic optimization problems.
- Design, coding and implementation of optimization models and algorithms in Python or C++. Familiarity with AMPL and/or Matlab is a plus.
- Preparation, presentation, and publication of academic research papers.
Candidates are expected to demonstrate strong academic performance during their undergraduate and graduate studies, as well as the ability to conduct high-quality independent research. Full-time dedication to the position is required.
The postdoctoral researcher will join the Department of Decision Sciences in the School of Business at the George Washington University and will work under the supervision of Professor Miguel Lejeune. The position will involve both independent research and collaboration with graduate students and other research collaborators.
If you are interested and/or want more information about the position, please contact Miguel Lejeune at
[email protected].
