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Computational Physics, Inc. Tech Stack

Precision timing and RF systems for government space and Earth observation

Research Services Springfield, Virginia 51–200 employees Founded 1984 Privately Held

Computational Physics, Inc. operates deep within the U.S. government R&D ecosystem, building and maintaining precision timing networks, RF/optical systems, and satellite ground data processing infrastructure. The tech stack is production-hardened government-grade (Red Hat, VMware, Ansible, Docker, Prometheus) rather than cloud-native, and active projects cluster tightly around timing-system design, validation, and integration—suggesting CPI is executing on a multi-year infrastructure modernization contract rather than exploring new markets.

Tech Stack 48 technologies

Core StackActive Directory Ansible Puppet Podman Docker Prometheus Grafana Elasticsearch MATLAB Python ArcGIS Java C++ Red Hat Enterprise Linux VMware vSphere SolarWinds Centrify Trellix Microsoft Defender for Endpoint rsyslog LVM XFS NFS Red Hat Satellite SELinux firewalld nftables ACAS LinkedIn Recruiter DNS+18 more

What Computational Physics, Inc. Is Building

Challenges

  • Maintaining secure, reliable network
  • Maintaining critical time dissemination equipment
  • Maintaining time transfer systems
  • Improving time transfer techniques
  • Improving astrometric and geodetic data products
  • Compliance demands of government contracting
  • Recruiting compliance with ofccp, eeo, far/dfars

Active Projects

  • Deployment and maintenance of precise timing and astrometry networks
  • Installation and testing of rf electronics and optical fiber systems for time dissemination
  • Maintenance of timing systems and hvac for environmental control
  • Rf electronics and timing system design
  • Rf over fiber integration
  • Atomic frequency standard validation
  • Design and implementation of twstt and ftt systems
  • Performance validation of twstt and ftt systems
  • Integration into existing timing infrastructure
  • Development and evaluation of future vlbi capabilities

Hiring Activity

Accelerating6 roles · 6 in 30d

Department

Engineering
4
HR
1
Research
1

Seniority

Mid
3
Senior
3
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About Computational Physics, Inc.

Computational Physics, Inc. is a privately held R&D contractor founded in 1984, headquartered in Springfield, Virginia. The company provides basic and applied science support to U.S. Department of Defense, civilian government agencies, federally funded research centers, universities, and defense primes. CPI's core competencies span atmospheric radiative transfer, remote sensing, ionospheric physics, space weather, and satellite ground data processing. Recent work centers on deployment and maintenance of precision timing and astrometry networks, RF electronics and optical fiber systems, and validation of two-way satellite time transfer and fast time transfer systems. The company operates within the compliance and contracting framework of government prime work (OFCCP, EEO, FAR/DFARS).

HeadquartersSpringfield, Virginia
Company Size51–200 employees
Founded1984
Hiring MarketsUnited States

Frequently Asked Questions

What is Computational Physics, Inc. working on?

CPI is actively developing and validating precision timing networks, RF/optical fiber systems for time dissemination, and future VLBI (very-long-baseline interferometry) capabilities. Projects include design of two-way satellite time transfer and fast time transfer systems, atomic frequency standard validation, and integration into existing timing infrastructure.

What tech stack does Computational Physics, Inc. use?

CPI operates on Red Hat Enterprise Linux, VMware vSphere, Ansible, Docker, Prometheus, Grafana, and Elasticsearch for infrastructure. Scientific and engineering work uses MATLAB, Python, ArcGIS, Java, and C++. Network and security tools include Active Directory, Trellix, Microsoft Defender, SolarWinds, and firewalld.

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How this profile is built

Computational Physics, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.