Postdoctoral Research Associate, Physics-Aware Deep Learning

About the School

The University of Virginia School of Data Science-the first of its kind in the nation-advances discovery, innovation, and societal impact through collaborative, open, and responsible data science research and education. Founded in 2019, the School brings together expertise across business, computation, engineering, humanities, law, mathematics, social sciences, statistics, and law to address complex, real‑world challenges. Its academic offerings include a B.S. in Data Science, an undergraduate minor, residential and online M.S. in Data Science programs, and a Ph.D. in Data Science, all designed to prepare students for a rapidly evolving data‑driven world.

About the Position

The Visual Intelligence Laboratory at the University of Virginia School of Data Science is looking for a postdoctoral researcher with specialties in physics-aware deep learning. A qualified candidate must hold a Ph.D. degree in data science, engineering, physics, applied mathematics, computer science, and other relevant areas and be able to conduct theoretical and applied research in physics-aware deep learning.

Key Responsibilities

* Conducting fundamental and applied research in physics-aware deep learning;

* Developing and maintaining high-quality Python software packages resulting from research;

* Disseminating research outcomes through journal publications and conference presentations;

* Reporting to funding agencies, including written and oral reports;

* Mentoring graduate and undergraduate students

Minimum Qualifications

* Doctoral degree (PhD or equivalent) in Data Science, mechanical/aerospace/civil engineering, materials science, physics, applied mathematics, and computer science, completed at the time of hire.

* Be a U.S. Person (U.S. citizen or permanent resident) and able to work in-person (Charlottesville, VA)

* Expertise in numerical methods such as finite element/volume method (FEM/FVM).

* Basic understanding of deep neural networks

Preferred Qualifications

* Expertise in high-performance computing and parallel computing

* Excellent communication skills

* Ability to work as a team

Physical Demands

This is primarily a sedentary job involving extensive use of desktop computing. Remote work is not allowed due to security concerns. The job does occasionally require traveling some distance to attend meetings and programs.

Position Details

This position will remain open until it is filled. This is a full-time in-person position at the School of Data Science at the University of Virginia in Charlottesville, VA. The initial appointment is for one year; however, the appointment may be renewed for an additional year contingent upon funding and satisfactory performance. This is an exempt level, benefited position. The candidate must be a U.S. citizen or permanent resident.

This position is not eligible for immigration sponsorship.

Salary Range: $65,000 - $70,000

Anticipated

Start Date:

August 2026

Health and Other Benefits (visit Health and Other Benefits for additional information)

* UVA Health Plan: the choice between 3 different health plans

* Vision Coverage

* Dental Plan

* Benefit Savings Plans

* Life Insurance

* Disability Benefits

* Paid Time Off: starting with 22 days of time off per year, 12 or more holidays, 8 weeks parental leave

Education Benefits (visit Education Benefits for additional information)

After six months of employment, full-time and part-time (20+ hours) employees in a benefits-eligible position are offered options of:

* Use of up to $5250 per calendar year towards a for-credit degree program or for-credit certificate program

* Use of up to $2000 of the total $5250 noted above per calendar year for professional development including job-related training, conferences, and initial certificate exams.

Application Process

Please apply online, and search for R0084663.

Complete an application online and attach:

Cover letter detailing your interest and relevant experience to this position Resume or CV Two letters of recommendation with contact information

Applications that do not contain all required documents will not receive full consideration.

Internal applicants:
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