Computational Scientist

Virginia Tech•Blacksburg, VA

5d•Onsite

About The Position

Advanced Research Computing (ARC) at Virginia Tech provides centralized, scalable compute, storage, and visualization services to support groundbreaking research across the university. ARC operates significant computing resources, including over 50,000 CPU cores, over 500 GPUs, more than 10 PB of storage, and an immersive visualization lab. The organization continuously evaluates emerging research computing trends to deliver high-impact systems tailored to the needs of the Virginia Tech research community. The Computational Scientist role involves direct collaboration with researchers, contributing to technical development, outreach, and administrative tasks. This position offers opportunities for mentoring students, contributing to open-source projects, attending national conferences, and participating in funded project proposals. ARC is committed to serving a diverse community and encourages applications from underrepresented groups in STEM.

Requirements

Master's degree, or a combination of education, training, and progressive experience that equates to a Master's degree.
Demonstrated service orientation and strong interpersonal skills.
Proven experience managing projects and organizing events.
Ability to explain technical concepts to audiences with varying levels of expertise.
Proficiency with Linux command line tools, shell scripting, and one or more programming languages commonly used in scientific computing (Python, C/C++, Fortran, R, etc.).
Experience working with HPC clusters and workflow orchestration tools.
Familiarity with software development best practices (version control, testing, documentation).
Exposure to data intensive or big data research workflows.

Nice To Haves

Proven record of organizing community oriented events (workshops, seminars, conferences).
Ph.D. or equivalent advanced degree with a focus on computational research.
Experience conducting computational research in a relevant field.
Background in one of the following domains (either is desirable): 1. Computational biology / bioinformatics / genomics, or 2. Computer Aided Engineering (CAE) for mechanical/aerospace applications (finite element analysis, finite difference methods, Lattice Boltzmann methods, or other numerical PDE solvers).
Deep knowledge of high performance computing, high speed networking, collaborative research tools, and storage architectures.