Job Description

The High-Performance Algorithms and Complex Fluids (HPACF) Group in the NREL Computational Science Center has an opening for a full-time Researcher in computational fluid dynamics with an emphasis in code development and deployment for detailed simulations of energy systems.

NREL is looking for a motivated researcher with a strong research background, particularly in the field of fluid dynamics, and with an interest in progressing NREL’s renewable energy mission through high-performance computing. The successful candidate will collaborate with other NREL researchers, other national laboratories, universities, and industrial partners in efforts to develop computational solutions at scale for real world questions and problems in renewable energy research.

The successful candidate will lead and contribute to a variety of original research projects while simultaneously pursuing funding in novel applications of CFD, machine learning and reduced order modeling that align simultaneously with the NREL mission and the vision for the HPACF Group. Projects and tasks for this position include, but are not limited to:

  • Developing data-driven and physics-informed reduced-order models of complex high-dimensional systems
  • Incorporating deep learning approaches to generate reduced dimensional representations of complex phenomena in diverse application areas, such as reacting flows, wind energy applications, and polymer upcycling strategies
  • Developing scientific software for high performance computing (HPC) applications in a team environment using agile software techniques to ensure quality and maintainability
  • Large eddy simulation (LES) model development for reacting and non-reacting turbulent flow applications and implementation in HPC environments
  • Analysis and visualization of extremely large, high-dimensional data sets seeking gain physical insight; incorporate extracted knowledge into data-driven models
  • Advanced use of data science tools, such as PyTorch, TensorFlow and Scikit-Learn, to rapidly develop data driven models for complex systems.
  • Writing, publishing, and presenting research results to a broad variety of audiences, both internal to NREL and to an international audience
  • Mentoring interns and postdoctoral researchers


Basic Qualifications

Relevant PhD . Or, relevant Master’s Degree and 3 or more years of experience . Or, relevant Bachelor’s Degree and 5 or more years of experience . Demonstrates complete understanding and wide application of scientific technical procedures, principles, theories and concepts in the field. General knowledge of other related disciplines. Demonstrates leadership in one or more areas of team, task or project lead responsibilities. Demonstrated experience in management of projects. Very good technical writing, interpersonal and communication skills.


Additional Required Qualifications

  • Experience working with large and complex datasets requiring HPC resources for analysis and manipulation
  • Experience with complex system modeling and analysis
  • Experience writing, programming, debugging, documenting and troubleshooting code for extreme scale computing hardware platforms in a team environment using continuous integration and Git source code management.
  • Research-focused doctoral degree or equivalent research experience.
  • Experience proposing, conducting, and publishing novel original scientific research and developing original research concepts and strategies.
  • Demonstrated ability to conduct research in a self-guided manner.
  • Ability to rapidly learn new skills and gain expertise in new research directions, and to build and interact effectively with diverse teams
  • Ability to manage projects from start to finish, including preparing proposals, budgeting, team management, timely completion of milestones, interfacing with internal/external collaborators/stakeholders, and authoring high impact publications.
  • Expert programming skills in C++ and MPI, and a working understanding of GPU accelerator programming using CUDA and HIP compilers.



Preferred Qualifications

  • Familiarity with foundational, statistical and machine learning concepts, optimization, regression models, uncertainty quantification, Bayesian analysis, model selection, clustering, outlier detection, etc.
  • Experience in data management on extreme-scale, high-dimensional data sets. Specifically, loading, filtering, organizing, transforming, and generally preparing large datasets for scientific analysis.
  • Demonstrated experience with large-scale numerical simulations of compressible, incompressible, and/or low Mach number reacting flow
  • Sufficient software engineering expertise to enable collaborative development of production-quality solutions: object-oriented design, coding, and testing patterns; engineering software platforms and large-scale data infrastructures. Candidates should also have experience programming in scripted languages, such as Python and R.
  • Background in relevant topics, especially computational fluid dynamics, on-shore/off-shore wind technologies, and renewable energy systems.
  • Ability demonstrate, through prior work, an ambition/enthusiasm for pursuing novel research directions in NREL/DOE mission critical areas.