Postdoc: Physics-Informed AI for Power Grids

University of Virginia; Charlottesville (VA); On-site; USD 65,000 - 81,000; Full time

Job summary

The University of Virginia's Department of Computer Science invites applications for a Research Associate (postdoctoral) position under Professor Ferdinando Fioretto.

You will develop physics-constrained generative AI methods for real-time transmission-grid topology control, publish in top ML and power systems venues, and collaborate with colleagues across academia, industry, and national laboratories.

Qualifications

• PhD required in CS, EE, OR or related field.
• Strong ML research track record with rigorous mathematical/experimental skills.
• Experience with generative/diffusion models, graph neural networks, and physics-informed ML preferred.

Responsibilities

• Lead technical deliverables and manuscript preparation for top-tier ML and power systems venues.
• Teach, mentor, and contribute to proposal development.
• Collaborate across academia, industry, and national laboratories.

Skills

Machine learning research

PyTorch proficiency

Graph neural networks

Power systems optimization

Education

PhD in CS/EE/OR or related field

Job description

The University of Virginia's Department of Computer Science invites applications for a Research Associate (postdoctoral) position under Professor Ferdinando Fioretto.

You will develop physics-constrained generative AI methods for real-time transmission-grid topology control, publish in top ML and power systems venues, and collaborate with colleagues across academia, industry, and national laboratories.