Job Description

Advanced Research Computing at Virginia Tech provides centralized, scalable compute, storage, and visualization services that enable groundbreaking research across all of Virginia Tech. ARC currently operates compute resources comprised of

50,000+ CPU cores
500+ GPUs
over 10 PB of storage
a world class immersive visualization lab.

We continuously evaluate emerging research computing trends to deliver high impact systems that meet the needs of the VT research community.

The Computational Scientist works directly with researchers at Virginia Tech while also contributing to technical development, outreach, and administrative activities. This role offers opportunities to mentor students, contribute to open source projects, attend national conferences, and participate in funded project proposals. ARC serves a diverse community and encourages applications from under represented groups in STEM.

Required Qualifications

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.

Preferred Qualifications

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.

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