Predoctoral Appointee - Machine Learning techniques for Optimization under Uncertainty
Our Math and Computer Sciences division (MCS) is seeking a Predoctoral Appointee to focus on Machine Learning techniques for Optimization under Uncertainty. The incumbent will develop machine learning technique to address the challenges of stochasticity in stochastic dynamic optimization and its application to energy and environmental applications. This involves prototyping learning architectures and integrate them with optimization tools to solve problems with constraints by scenario-based optimization or policy iteration methods. The position involves balance between documentation, method designing, method prototyping, and validation and requires interaction with a diverse team of scientists and environmental and electrical engineers.
- MS in one of the mathematical sciences (Applied Math, Math, Statistics, Computer Sciences, Operations Research) or a relevant engineering area
- Experience with python and R
- Coursework in optimization, statistics, and machine learning
- Experience with Julia and Matlab
- Research experience in Optimization or Machine Learning
Job FamilyTemporary Family
Job ProfilePredoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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