Research Associate

University of Texas at Austin•Austin, TX

1d•$90,000•Onsite

About The Position

The Texas Advanced Computing Center (TACC) at The University of Texas at Austin is one of the leading supercomputing centers in the world, supporting advances in computational research by thousands of researchers and students. TACC staff help researchers and educators use advanced computing, visualization, and storage technologies effectively, and conduct research and development to make these technologies more powerful, more reliable, and easier to use. TACC staff also educate and train the next generation of researchers, empowering them to make discoveries that advance knowledge and change the world. TACC is currently preparing for the deployment and operation of Horizon, NSF’s next-generation leadership-class GPU system based on NVIDIA accelerator technologies. Horizon will significantly expand TACC’s capabilities in large-scale simulation, data analytics, and AI-driven workloads, requiring advanced expertise in performance portability, accelerator programming, and scalable application design. If you are not sure that you’re 100% qualified, but up for the challenge – we want you to apply. We believe skills are transferable and passion for our mission goes a long way. Candidates will need to upload a resume, letter of interest, unofficial copy of transcript, and the names of three references to apply for this position. UT Austin offers a competitive benefits package that includes: 100% employer-paid basic medical coverage Retirement contributions Paid vacation and sick time Paid holidays Please visit our Human Resources (HR) website to learn more about the total benefits offered. The Research Associate for High Performance Computing (HPC) will contribute to TACC’s HPC research, development, and user support efforts.

Requirements

Doctoral degree in a relevant academic or research discipline.
Documented research experience with high‑performance computing, including at least two related publications.
Proficiency with core HPC programming languages and paradigms, including C/C++, Fortran, and MPI.
Proficiency in GPU-accelerated HPC programming, with emphasis on CUDA and modern accelerator programming models (e.g., OpenMP offload).
Proficiency in Python for scientific computing, including integration with machine learning and AI frameworks such as PyTorch, particularly in GPU-accelerated environments.
Experience in developing parallel applications.
Demonstrated success working in collaborative, interdisciplinary research environments with computational scientists and engineers.
Strong interpersonal communication skills and a professional demeanor.
Relevant education and experience may be substituted as appropriate.

Nice To Haves

Experience debugging, profiling, and optimizing fluid dynamics (CFD) applications on CPU and GPU-accelerated systems.
Experience optimizing algorithms for accelerator-based systems using CUDA and OpenMP offload.
Experience programming for Grace Hopper and next-generation NVIDIA GPU architectures.
Experience or strong interest in teaching and producing HPC training resources related to accelerator-based programming.

Responsibilities

Work with TACC users to integrate HPC technologies into their research and development activities and to measure the impact of TACC's HPC systems on their research capabilities.
Perform application performance analysis and industry standard benchmarking on HPC systems.
Evaluate new HPC architectures, systems, and software tools — including emerging GPU-centric systems such as Horizon — to determine best practices and identify future acquisition targets.
Assist TACC users with the analysis and optimization of their research software on TACC’s HPC systems, with particular emphasis on GPU-accelerated and heterogeneous architectures such as Horizon.
Support the transition of scientific applications from traditional CPU-based systems to large-scale GPU systems, enabling users to effectively leverage next-generation architectures.
Initiate and conduct research into new high performance and parallel computing applications, techniques, tools, and algorithms.
Publicize the results of these activities through presentations at conferences/workshops and through publications in proceedings and professional journals.
Pursue and obtain external funding to enhance TACC's HPC activities, expertise, and resources by submitting proposals to funding agencies, industrial partners, and vendors directly or in partnership with researchers and faculty at UT Austin or other institutions.