Senior Navigation Analyst

Description

Do you have a deep understanding of navigation systems and are you eager to apply that knowledge to solve unique navigation challenges facing the US Navy?

Are you passionate about developing the next generation of technical experts in the timely, and timeless, art of navigation?

Do you want to be part of a team of engineers, applied mathematicians and physicists as we ensure the US Navy has the most accurate, yet reliable maritime navigation systems in the world?

• Become the Subject Matter Expert on the mathematical theory and physics behind navigation systems that must operate in extremely demanding conditions in support of the most crucial missions of the US military.
• Apply this knowledge to model the errors in various navigation systems and be part of a team determining their impact on tactical and strategic mission objectives as we also investigate methods to mitigate their influence.
• Join a highly talented and motivated team of mathematicians, engineers and physicists focused on navigation error analysis with many opportunities to collaborate with other innovative experts from a variety of technical fields.

• Mastering the mathematical models governing the performance of the most accurate inertial navigation systems in the US Navy surface and subsurface fleet.
• Deriving the physics-based models for how the various supporting aids to the navigation system interact and influence the inertial system.
• Assessing the fidelity of the navigation systems being studied then communicating your conclusions and recommendations to decision-makers.
• Mentoring colleagues to grow subject matter expertise in mathematical physics-based modeling for navigation sensors.

You meet our minimum qualifications for the job if you…

• Have a Bachelor's degree in Mathematics, Physics, Engineering or similar technical field.
• Have at least 7 years of relevant experience developing and using mathematical or physics-based models describing a complex system to perform analysis for military applications.
• Have specific experience with performance analysis for maritime navigation or missile guidance systems.
• Have experience modeling navigation sensors including IMUs, GPS, or navigational sonars (Doppler Velocity Log, Correlation Velocity Log).
• Have experience with and a passion for mentoring/knowledge sharing.
• Have experience with Kalman filters and/or modern system estimation and multi-sensor data fusion methods.
• Have experience with a high-level programming language like MATLAB or Python.
• Have the verbal and writing skills and disposition necessary for documenting and presenting complex technical work to APL colleagues and government sponsors at a broadly understandable level.
• Enjoy solving challenging technical problems requiring both self-motivated independent investigation and collaborative team effort.
• Are willing and able to travel occasionally to US Navy facilities, bases and contractor sites to present results.
• Are able to obtain a Secret level security clearance by your start date and can ultimately obtain a Top Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

• Have a Master's or PhD in Mathematics, Physics, Engineering or similar technical field.
• Have specific experience with engineering or test and evaluation for the Trident II, Minuteman III, Sentinel, or AN/WSN-7 systems.
• Have proven experience developing or executing technical training or curricula for STEM-related topics.
• Have a Top Secret security clearance.

The Johns Hopkins University Applied Physics Laboratory (APL) brings world‑class expertise to our nation’s most critical defense, security, space and science challenges. While we are dedicated to solving complex challenges and pioneering new technologies, what makes us truly outstanding is our culture. We offer a vibrant, welcoming atmosphere where you can bring your authentic self to work,…