PhD ML Research Scientist — National Security Systems

The Johns Hopkins University Applied Physics Laboratory; Laurel (MD); On-site; USD 105,000 - 245,000; Full time

Job summary

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a PhD graduate with strong ML/AI expertise to support multi-disciplinary teams in defense and national security applications. You will contribute to data collection, processing, and analysis to evaluate performance of strategic systems throughout their lifecycle, collaborating with engineers, statisticians, and data scientists.

Applicants should have a deep ML foundation and experience with libraries like PyTorch or

Qualifications

• PhD in Data Science, Statistics, Physics, Mathematics, Computer Science or related field.
• Strong interoperability and ability to work both independently and in teams.
• Solid mathematical foundation for ML (probability, statistics, linear algebra).

Responsibilities

• Collaborate with multi-disciplinary teams to develop data collection, processing, and analysis to evaluate performance of systems supporting Navy and Air Force.
• Lead the full research lifecycle: model/algorithm selection, experimentation, analysis, and result presentation.
• Apply ML expertise to select modeling approaches for complex real-world data.
• Leverage internal funding to shape future research directions.
• Communicate technical results through papers and presentations to technical staff and decision makers.

Skills

PhD in ML/Statistics/Data Science

Independent work

Team collaboration

Security clearance readiness

Effective communication

Education

PhD in Data Science, Statistics, Physics, Mathematics, CS

Tools

PyTorch

TensorFlow

Job description

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a PhD graduate with strong ML/AI expertise to support multi-disciplinary teams in defense and national security applications. You will contribute to data collection, processing, and analysis to evaluate performance of strategic systems throughout their lifecycle, collaborating with engineers, statisticians, and data scientists.

Applicants should have a deep ML foundation and experience with libraries like PyTorch or