2024 PhD Graduate - System Modeling, Evaluation, and Planning
Laurel, MD 
Share
Posted 12 days ago
Job Description
Description

Are 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 groupyou 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.
  • Evaluate system-level accuracy of current strategic weapon systems and support the design and engineering of future systems to meet mission needs
  • Apply artificial intelligence, machine learning, and data fusion methodologies to complex, real-world problems and build robust computational frameworks to support advanced data-centric analyses
  • Advance the state of the art in mission planning by developing software and implementing vehicle dynamics models for new and prototype weapon systems
  • Develop operational concepts for new weapon systems that leverage advances in estimation and optimization
  • Function as part of a multi-disciplinary team to analyze the nuclear survivability of strategic weapon systems and structures

Qualifications

You meet our minimum qualifications for the job if you...

  • Possess a PhD in applied math, applied statistics, physics, engineering, or a closely related field
  • Are skilled in scientific programming using a high-level language, such as C++, MATLAB, or Python
  • Possess outstanding technical written and oral communication skills
  • Are willing and able to occasionally travel to meetings and sponsor sites
  • Are able to acquire an Interim Secret level security clearance by your start date and can ultimately acquire a final 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.

You 'll exceed our minimum requirements with experience in any of the following...

  • Statistical analysis, parameter estimation and inverse problems, Kalman filtering, or maximum likelihood estimation using Fisher information
  • Optimal control, numerical optimization, and trajectory design
  • Prior experience with tactical or strategic missile systems
  • Prior experience with inertial navigation systems
  • Have expertise in High Energy Particle Physics, Thermal Mechanical Engineering, Chemical Physics, Nuclear Engineering, Nuclear Weapon Effects, or Plasma Physics
  • Hold an active Secret or higher level clearance

Why work at APL?

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, continue to grow, and build strong connections with inspiring teammates.

At APL, we celebrate our differences and encourage creativity and bold, new ideas. Our employees enjoy generous benefits, including a robust education assistance program, unparalleled retirement contributions, and a healthy work/life balance. APL's campus is located in the Baltimore-Washington metro area. Learn more about our career opportunities at www.jhuapl.edu/careers.


About Us

APL is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, creed, color, religion, sex, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, genetic information, veteran status, occupation, marital or familial status, political opinion, personal appearance, or any other characteristic protected by applicable law.

APL is committed to promoting an innovative environment that embraces diversity, encourages creativity, and supports inclusion of new ideas. In doing so, we are committed to providing reasonable accommodation to individuals of all abilities, including those with disabilities. If you require a reasonable accommodation to participate in any part of the hiring process, please contact Accommodations@jhuapl.edu. Only by ensuring that everyone's voice is heard are we empowered to be bold, do great things, and make the world a better place.


The Johns Hopkins Applied Physics Lab (APL) is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual identity, gender identity, national origin, disability, or protected Veteran status.

 

Job Summary
Start Date
As soon as possible
Employment Term and Type
Regular, Full Time
Required Education
Doctorate
Required Experience
Open
Email this Job to Yourself or a Friend
Indicates required fields