PhD Data Scientist — ML for National Security

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

Job summary

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a PhD graduate with expertise in machine learning to support multi-disciplinary teams tackling quantitative tasks for defense and national security applications.

You will join engineers, scientists, statisticians and analysts committed to advancing performance evaluation of national weapons systems. APL emphasizes innovation, integrity, trust, and teamwork.

Qualifications

• Earned PhD in a relevant field and demonstrate strong mathematical foundations for ML.
• Experience with ML libraries (PyTorch, TensorFlow) and adapting methods.
• Proven ability to select appropriate modeling approaches for supervised/unsupervised/reinforcement learning.

Responsibilities

• Work with multi-disciplinary teams to support data collection, processing and analysis for defense-related systems.
• Contribute to full R&D lifecycle including model/algorithm selection, experimentation, analysis and presentation of results.
• Apply statistical and ML expertise to select modeling approaches for complex real-world data.
• Leverage internal funding to shape future research directions.
• Communicate technical knowledge through papers and presentations to technical staff, management and government decision makers.

Skills

Interpersonal skills

ML foundations (probability, stats, LA

ML libraries (PyTorch/TensorFlow)

Modeling techniques—supervised/unsuper

Security clearance eligibility

Education

PhD in Data Science, Statistics, Physics, Mathematics, Computer Science or related field

Job description

The Johns Hopkins University Applied Physics Laboratory (APL) is seeking a PhD graduate with expertise in machine learning to support multi-disciplinary teams tackling quantitative tasks for defense and national security applications.

You will join engineers, scientists, statisticians and analysts committed to advancing performance evaluation of national weapons systems. APL emphasizes innovation, integrity, trust, and teamwork.