About this role
The School of Engineering and Applied Science at the University of Virginia is seeking qualified candidates for 3 Postdoc positions in a newly established multidisciplinary research center. Research at the Center is focused on supercomputer simulations of extreme conditions experienced by hypersonic vehicles and quantifying the overall uncertainty. The candidate will assume the role of a software developer in the Computational Fluid Dynamics (CFD) and Propulsion Laboratory, a participating lab in the Center. The research scientist will be responsible for the development and maintenance of predictive software components, support integration into the full system, and assist with full-system testing and analysis.
The candidate can expect to work with a multidisciplinary and multi-university team involving engineers, computer scientists, and mathematicians constructing a full-system package comprising predictive software, data analytics, and machine learning. The candidates will work closely with and report to Dr. Xinfeng Gao.
The candidates must work closely with the Software Architect in developing and implementing plans for integrating software libraries performing I/O, adaptive mesh-refinement, data assimilation, and AI/ML in a manner that supports the participation of postdocs and graduate students. The candidate may be expected to supervise other postdocs and mentor PhD students in aspects of software development and usage.
QUALIFICATION REQUIREMENTS
Candidates must hold a Ph.D. in Mechanical Engineering, Aerospace Engineering, Computational & Applied Mathematics, or a closely related discipline with research experience in computational science and engineering by the start date. The successful candidate will demonstrate deep expertise in developing high-performance scientific software for massively parallel, heterogeneous supercomputing architectures, and a record of contributing to large-scale simulation codes for cutting-edge engineering applications.
Required technical proficiencies include advanced C++ and parallel programming models such as MPI and OpenMP. Candidates must also have hands-on experience with GPU programming using CUDA, HIP, or performance-portability frameworks such as Kokkos or RAJA. Candidates should have a strong command of numerical methods for partial differential equations, including high-order spatial discretizations, shock-capturing schemes, implicit and explicit time integration, and scalable linear and nonlinear solvers. Demonstrated experience with performance profiling, optimization, and scalability analysis on leadership-class computing systems is expected.
The candidates must have a proven ability to develop complex simulation software within multidisciplinary teams, applying modern software engineering practices: version control, continuous integration, automated testing, and code verification and validation. Effective communication and coordination with computing-facility technical staff on resource planning, code deployment, and production workflows is also required.
Preferred Qualifications
Preference will be given to candidates with expertise in physics-based modeling and simulation of compressible and turbulent flows. Relevant areas include direct numerical simulation (DNS), large-eddy simulation (LES), Unsteady Reynolds-averaged Navier–Stokes (URANS) approaches, and hybrid URANS/LES approaches. Experience with hypersonic flows is highly desirable, particularly hypersonic boundary layers, laminar-to-turbulent transition, shock–boundary-layer interactions, and thermochemical nonequilibrium and finite-rate chemistry. Also desirable is experience with multi-fidelity modeling strategies, including reduced-order models, data-driven or machine-learning-augmented closures, surrogate modeling, and uncertainty quantification. Familiarity with multiphase flows, reacting flows, adaptive mesh refinement, or mapped multiblock techniques is a plus.
Candidates with focused expertise or interests in these topics are highly encouraged to apply:
- i) Developing moving mapped multiblock techniques for complex geometries ii) Integrating embedded boundary and mapped multiblock techniques with adaptive mesh refinement for high-order CFD algorithms iii) Implementing wall-modeled LES and wall-resolved LES for high speed flows over complex geometries iv) Developing space-time parallel algorithms
- v) Developing quantum algorithms for Boltzmann equations
Estimated salary range is $65,000 - $85,000, commensurate with experience and qualifications of the candidates.
APPLICATION PROCEDURE
Apply online through UVA Jobs and search for position number R0087831. Attach a cover letter, a curriculum vitae, and contact information for three references. Please note that multiple documents can be uploaded in the box.
APPLICATION DEADLINE
Review of applications will begin on November 1, 2026, but the position will remain open until filled. The University will perform background checks on all new hires prior to employment.
Questions regarding this position should be directed to Professor Xinfeng Gao, at [email protected] .
For questions about the application process, please contact Richard Haverstrom, Academic Recruiter, at [email protected] .
For more information on the benefits available to Research Associates at UVA, visit 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.