Description
Are you passionate about applying your knowledge of statistical parameter estimation and mathematical modeling to solve real world problems?
Are you interested in ensuring our nation's future strategic deterrence capability?
Do you love to solve challenging problems and aren't afraid to think outside the box?
Do you enjoy developing expertise in new technical areas to solve complex problems?
Would you like to be part of a culture that promotes employee growth, collaborative innovation and inspired leadership?
If so, we're looking for someone like you to join our team at APL.
The Weapon System Modeling, Evaluation and Planning Group within the Force Projection Sector of the John Hopkins Applied Physics Laboratory is seeking a Weapon System Modeling and Estimation Researcher to help us evaluate system-level accuracy of the nation's most significant strategic deterrents. The ideal candidate will work on complex, multi-discipline problems with a team of experts to offer technical contributions to improve current and next generation U.S. Strategic Systems.
As a Researcher in the Weapon System Modeling, Evaluation and Planning Group You Will: Contribute to the design and development of our nation's future sea-based and ground-based strategic weapon systemsPerform modeling and simulation studies to assess subsystem performance characteristics and their effects on total weapon system accuracy Innovate and implement statistical estimation and physics-based analysis methodologies to evaluate and improve the accuracy performance of submarine launched and intercontinental ballistic missilesShape flight test instrumentation performance requirements to ensure that future weapon system Initial Operational Test and Evaluation (IOT&E) and Follow-on Operational Test and Evaluation (FOT&E) accuracy objectives are metProvide tactically relevant predictions of Trident II accuracy estimated from actual flight test data to United States Strategic CommandLearn the physics of submarine launched and intercontinental ballistic missiles including guidance, navigation and reentry aerodynamics Use elements of mathematics, physics, statistics, computer science, and application-specific knowledge to gather, make, and communicate principled conclusions from data to internal and external stakeholders Qualifications You meet our minimum qualifications for the Weapon System Modeling and Estimation Researcher position if you... Possess a PhD in applied math, applied statistics, physics, engineering, or a closely related fieldHave solid knowledge of statistical foundations (e.g. distributions, parameter estimation with confidence bounds, etc...)Have professional experience applying statistical methods to data, such as non-parametric tests, maximum likelihood estimators, or Bayesian methods.Are skilled in scientific programming using a high-level language, such as C++, MATLAB, or PythonHave strong attention to detail and a commitment to minimizing errorsAre a natural self-starter who prides themselves on delivering quality productsPossess outstanding written and oral communication skillsEffectively collaborate with all levels of staff, external collaborators and sponsorsAre willing and able to occasionally travel to meetings and sponsor sitesAre 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... Hold an active Secret security clearance or higherHave research, development, or analysis experience in some or all of the following:Submarine launched or intercontinental ballistic missile accuracy assessmentStatistical signal processing and/or stochastic processesDetection and estimation theoryKalman filtering methods and state space modelingModeling and simulation techniques Probability theory System identification methodsSpectral estimation and fitting methodsInertial navigation and guidance algorithmsNext generation inertial measurement units and sensorsMachine learning algorithms and data mining 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 http://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 ******. 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.
