2027 PhD Graduate – Engineer or Scientist - Critical Infrastructure Protection

Johns Hopkins Applied Physics Laboratory•Laurel, MD

About The Position

The Critical Infrastructure Protection Group at the Johns Hopkins University Applied Physics Laboratory seeks a 2027 PhD graduate to develop, evaluate, and transition technologies that strengthen the security and resilience of critical infrastructure and cyber-physical systems. The work spans operational technology, industrial control systems, cyber situational awareness, resilient architectures, system assurance, machine learning, AI-enabled cyber defense and decision support, and digital twins. It may also include software and hardware reverse engineering, vulnerability research, and proof-of-concept offensive cyber capabilities focused on critical infrastructure systems. The role covers the technology lifecycle: understanding mission challenges, conducting research, building prototypes, evaluating them on representative systems, and transitioning successful capabilities into operationally relevant environments. Work may use laboratory facilities, including the CYPRESS Laboratory, to evaluate technologies against realistic constraints and threats. The engineer works directly with mission stakeholders and contributes to multidisciplinary teams.

Requirements

Have or be near completion of a PhD in cybersecurity, computer science, engineering, or another relevant technical field, or have equivalent practical experience.
Be skilled in or knowledgeable about Python or another programming language such as C/C++, Java, Go, or Rust; have shell scripting and version control experience; and be comfortable developing, debugging, and automating technical workflows in Linux environments.
Have knowledge of offensive cyber capability development.
Have working knowledge of IP networking and familiarity with one or more of virtualization, software-defined networking, embedded systems, industrial control systems, or industrial communication protocols.
Understand and apply cybersecurity principles such as system and network security, threat modeling, vulnerability analysis, defense-in-depth, and security architecture to software, hardware, networks, or cyber-physical systems.
Be comfortable working on open-ended technical problems and moving from problem formulation and experimentation to implementation, integration, testing, and evaluation.
Be able to formulate and investigate technical questions experimentally and use testing evidence to refine designs and technical approaches rather than only implementing predefined requirements.
Have hands-on experience using generative AI or AI-assisted development tools for technical tasks such as coding, debugging, testing, analysis, or documentation; verify generated results through testing, checking assumptions and calculations, and comparing outputs with source data or authoritative references; and use AI tools in accordance with applicable information-handling requirements.
Have strong written and verbal communication skills and be able to work effectively on cross-functional, interdisciplinary teams.
Have experience with debugging interfaces, diagnostic tools, and protocol analysis, and understand the strengths and weaknesses of security mechanisms.
Be able to obtain an Interim Secret clearance by the start date and ultimately obtain a Top Secret clearance. Eligibility for access to classified information requires U.S. citizenship and a government security clearance investigation.

Nice To Haves

Hands-on cybersecurity experience with operational technology, industrial control systems, embedded systems, or other cyber-physical systems.
Experience with programmable logic controllers (PLCs), industrial automation platforms, or associated engineering environments.
Experience with cyber test and evaluation, software or hardware reverse engineering, vulnerability research, adversary emulation, or network protocol analysis.
Experience developing, integrating, securing, or evaluating AI/ML capabilities, particularly for cybersecurity applications involving time-series or sensor data, anomaly detection, autonomous systems, cyber situational awareness, or decision support.
Experience evaluating AI/ML systems for robustness, security, uncertainty, failure modes, or performance under adversarial, degraded, or out-of-distribution conditions.
Experience designing experiments, defining quantitative evaluation criteria, analyzing results, and drawing defensible conclusions from complex system testing.
Experience with cyber engineering, analysis, and development tools such as Wireshark, Ghidra, Docker, SIEM tools, or comparable technologies.
Familiarity with modeling and simulation, data science, cryptography, formal methods, system assurance, resilient architectures, or digital twin technologies.
Research experience, publications, technical leadership experience, or experience transitioning prototypes into operational use.
Active TS/SCI or higher-level U.S. Government security clearance.

Responsibilities

Research, develop, and prototype technologies that improve the security, resilience, and assurance of critical infrastructure and cyber-physical systems, including autonomous cyber defense technologies.
Work with sponsors, operators, and other mission stakeholders to understand operational challenges, define technical requirements and success criteria, and translate mission needs into technical solutions.
Build and evaluate cybersecurity capabilities across software, networks, embedded devices, industrial control systems, and representative cyber-physical environments.
Analyze and reverse engineer control system protocols, software, hardware, and system architectures to identify vulnerabilities and opportunities for cyber hardening and improved resilience.
Conduct cyber test and evaluation, adversary emulation, vulnerability research, protocol analysis, and experimental assessments in laboratory and representative operational environments.
Develop and evaluate cyber situational awareness, decision support, resilient architecture, and AI-enabled capabilities; use AI/ML for analysis and decision support where appropriate and characterize performance, uncertainty, limitations, and failure modes under realistic conditions.
Evaluate commercial and emerging technologies, identify gaps affecting government and mission use cases, and adapt or extend technologies to address operational needs.
Transition successful prototypes from laboratory demonstrations into operationally relevant environments and, when appropriate, fielded capabilities.
Communicate technical approaches and results through documentation, briefings, demonstrations, and, when appropriate, publications and technical forums.