Internship - Stochastic Model Predictive Control Generative Models Smart Building Control

Position: Internship - Stochastic Model Predictive Control with Generative Models for Smart Building Control
MERL is looking for a research intern to develop efficient transformer-informed stochastic MPC to control net-zero energy buildings. This is an exciting opportunity to make a real impact in the field of cutting-edge deep learning and predictive control on a real system. Publication of results produced during the internship is desired. The expected duration of the internship is 3-6 months with a flexible start date.

The Ideal Candidate Will Have:

* Significant hands-on experience with stochastic MPC

* Publications in SMPC are a strong plus

* Fluency in Python and Py Torch

* Understanding of probabilistic time-series prediction

* Experience with convex programming

* Convex formulations of MPC/SMPC problems are a strong plus

* Completed their MS, or >50% of their PhD program

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Working at MERL requires full authorization to work in the U.S and access to technology, software and other information that is subject to governmental access control restrictions, due to export controls. Employment is conditioned on continued full authorization to work in the U.S and the availability of government authorization for the release of these items, which might include without limitation, obtaining an export license or other documentation.

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