2026 PhD Graduate - System Modeling, Evaluation, and Planning
Johns Hopkins APL · Laurel, MD · Mathematics
About this role
Johns Hopkins APL is hiring a junior-level Data Scientist based in Laurel, MD. The posting calls out experience with Machine Learning. Listed education preference: a Ph.D. or equivalent.
- Role
- Data Scientist
- Function
- data engineering
- Level
- junior
- Track
- Individual contributor
- Location
- Laurel, MD
- Education
- Ph.D. preferred
- Department
- Mathematics
- Posted
- Aug 29, 2025
More roles at Johns Hopkins APL
Job description
from Johns Hopkins APL careersAre you interested in applying your STEM background to strategic deterrence and defense?
Are you interested in defining methodologies that will be used for mission planning and test and evaluation of current and future weapon systems?
Do you like contributing to complex efforts that require team-based approaches?
If you have a PhD in math, physics, engineering, or computer science, we're looking for someone like you to join our team at APL.
The System Modeling, Evaluation, and Planning Group is seeking Weapon System Analysts and Software Engineers to assess the planning and evaluation the nation’s primary strategic deterrents. You will be joining a hardworking team of engineers, physicists, and mathematicians who are passionate about their role as an independent evaluator for the nation’s strategic systems. We strive to foster an environment of innovation and learning to develop the critical technologies and experts of the future.
As an analyst in the System Modeling & Estimation group you may work on one or more of the following...
- Learn about the principals of inertial navigation systems, including accelerometers and gyroscopes, and leverage data collected during flight tests and ground tests to estimate the underlying, physics-based errors in these systems.
- Learn about the dynamics of missile systems, reentry systems, and their associated fuzing mechanisms, and leverage data collected during flight tests and ground tests to estimate the underlying, physics-based errors in these systems.