X (Tapestry, Alphabet)
Mountain View, CA, US
2026 PhD Residency, Machine Learning for Grid Simulations (Tapestry)
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About the role
This PhD residency focuses on advancing machine-learning-driven approaches for power grid simulations, with an emphasis on improving the speed, accuracy, and numerical stability of large-scale transient simulations. You will work closely with power systems and simulation experts to apply modern ML techniques to real-world grid modeling challenges.
How you will make 10X Impact
- Accelerate grid simulations by developing machine learning models that reduce computational cost while preserving physical accuracy.
- Advance state-of-the-art ML methods for modeling complex grid components, building on and improving existing simulation baselines.
- Partner with power system experts to integrate ML techniques directly into simulation workflows used for planning, reliability analysis, and outage prevention.
- Improve numerical stability and performance of transient simulations by applying physics-informed and data-driven modeling approaches.
- Deliver production-ready research outputs that strengthen core simulation technology and influence future product direction.