Details: Jacobs ESTS Group offers a partnership in which you can grow personally and professionally with the advantages of strong leadership, competitive compensation, and rewarding career paths. Come join the team whose work is destined to have a long-range effect on future generations!Successful candidate will define the meteoroid environments needed for spacecraft designs and risk assessments/mitigations. Focus shall be on dynamical modeling of natural particles in the threat regime (typically >0.1mm in diameter) and the observations needed to calibrate these models. Successful candidate will be responsible for improvements to NASA's Meteoroid Engineering Model (MEM) and the Meteoroid Environments Office (MEO) meteor shower forecasting codes. Therefore, ability to update the MEM model with improved sporadic source distributions calibrated to various observational data sets, conducting studies that could extend its range beyond the inner Solar System, and assisting in the production of meteor shower forecasts is required. Successful candidate will also support the MEO in improving the determination of meteor properties/trajectories/orbits, especially in respect to optical systems. Knowledge of programming languages such as Fortran, C, C++, IDL, etc. is required. Knowledge of the different types of propagators (such as Radau, sympletic, etc.) is highly desired. Publications in the field is also desired. Successful candidate will be able to work independently on specific tasks, but also to contribute to group efforts and to understand the relationship between their results and the overall, system-scale engineering requirements. Masters in Physics, Astronomy, or a related discipline from an accredited institution with a minimum of 2 years experience. Bachelors with 6 years of experience will be considered. PhD preferred. Successful candidate shall have experience in astronomical dynamics or celestial mechanics involving small natural bodies (dust, meteoroids, asteroids, comets) and in integrating/comparing astronomical observations with dynamical models.
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