Research Associate in Computer Science

The Rector & Visitors of the University of Virginia; Charlottesville (VA); On-site; USD 55,000 - 74,000; Full time

Job summary

The Rector & Visitors of the University of Virginia in Charlottesville invites applications for a postdoctoral Research Associate in the Department of Computer Science. The successful candidate will work with Professor Ferdinando Fioretto on physics-constrained generative AI methods for power systems and diffusion models for topology control.

The appointment is one year with potential renewal, focusing on research, teaching, mentoring, and proposal development, with opportunities to publish in

Qualifications

• PhD by start date in CS/EE/OR or related field.
• Strong ML research record with rigorous mathematical and experimental skills.
• Expertise in generative/diffusion models, optimal power flow/power systems, graph neural networks, and physics-informed ML. Proficiency with PyTorch.

Responsibilities

• Lead technical deliverables and manuscripts for top ML conferences/journals.
• Develop physics-constrained generative AI methods for real-time topology control.
• Collaborate across academia, industry, and national labs and contribute to teaching/mentoring.

Skills

Machine learning research

PyTorch

Graph neural networks

Diffusion models

Power systems

Education

PhD in Computer Science, Electrical Engineering, OR

Tools

PyTorch

Python

Job description

The University of Virginia, Department of Computer Science, is seeking applicants for a Research Associate (postdoctoral) position under the supervision of Professor Ferdinando Fioretto.

As a key member of the research team, the postdoctoral researcher will develop novel physics-constrained generative AI methods (diffusion models and flow matching) for real-time transmission-grid topology control. The researcher will design topology-agnostic diffusion models that generate diverse switching configurations from post-contingency grid states while satisfying network connectivity, N-1 security, and thermal limits. Concurrently, they will enhance their skills in teaching, mentoring, and proposal development.

The successful candidate will lead technical deliverables and manuscripts targeting top-tier machine-learning conferences and leading journals in power systems. The position offers an exceptional collaborative network spanning academia, industry, national laboratories.

QUALIFICATION REQUIREMENTS: A Ph.D. in computer science, electrical engineering, operations research, or a related field by the start date. The candidate should have a strong machine-learning research record, rigorous mathematical and experimental skills, and the ability to deliver high-quality results on short timelines. Expertise in one or more of the following is strongly preferred: generative or diffusion models, optimal power flow and power systems, graph neural networks, constrained or physics-informed machine learning. Proficiency with frameworks such as PyTorch is expected. Prior power-systems experience is highly beneficial.

APPLICATION DEADLINE: Review of applications will begin on August 15, and the position will remain open until filled. The University will perform background checks on all new hires prior to employment.

This is a one-year appointment; The appointment may be renewed for an additional year contingent upon available funding and satisfactory performance. The successful candidate is expected to begin by September 1, 2026, or earlier.

For questions regarding this position, contact Ferdinando Fioretto, Associate Professor, at [e-mail].

For questions regarding the application process, contact Rich Haverstrom, Faculty Search Advisor, at [e-mail].

For more information on the benefits available to postdoctoral associates at UVA, visit postdoc.virginia.edu and hr.virginia.edu/benefits.

The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment.