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Interning At Nasa – Inner Planets Mission Design Postdoc In Friendswood

    Website NASA

    Job Description:

    New ideas are all around us, but only a few will change the world. That’s our focus at JPL. We ask the biggest questions, then search the universe for answers—literally. We build upon ideas that have guided generations, then share our discoveries to inspire generations to come. Your mission—your opportunity—is to seek out the answers that bring us one step closer. If you’re driven to discover, create, and inspire something that lasts a lifetime and beyond, you’re ready for JPL.

    Job Responsibilities:

    • This postdoctoral opportunity seeks to find a motivated individual to develop trajectory design and optimization capabilities for missions in the Inner Solar System, as well as non-gravitational force modeling and/or calibration analysis using accelerometer data from the currently operating GRACE-FO mission.
    • The postdoctoral scholar will work with (1) Try Lam, Group Supervisor for the Inner Planets Mission Analysis Group, and his team members to design and implement a formation flying strategy using low thrust propulsion in low Earth orbit, and (2) Felix Landerer, GRACE-FO Project Scientist, to develop, test, and optimize thermal calibration models for the GRACE-FO accelerometers.
    • The postdoctoral researcher will be encouraged to develop their own research with opportunities to use the software infrastructure at JPL.
    • They will be supported in carrying out the research project, conducting presentations at professional meetings, and publishing their work in collaboration with their postdoc advisors.

    Job Requirements/Qualifications:

    • We welcome applicants who have a Ph.D. in Aerospace Engineering, Astronautical/Aeronautical Engineering, Physics, Mathematics, or related technical discipline, and have broad knowledge in astrodynamics, mission design, trajectory design, and optimization.
    • Experience with stochastic optimal control for the design of robust trajectories under models of uncertainty is strongly recommended.

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