Earth. Conduct observational data analysis that supports model validation. Incorporate additional physical schemes into numerical models. Develop and grow an independent research program through external program support. Participate in mission and instrument proposals to support the strategic growth of the Department.
Daily and Monthly Responsibilities: Independently develop and implement hypotheses-driven numerical model and data analysis activities; Configure, initialize, run, and analyze atmospheric numerical models of Earth, Mars, Titan, and other terrestrial planetary atmospheres to test hypotheses. Evaluate model results for physical consistency and plausibility; identify interesting,
new, or otherwise noteworthy results; place results in the context of prior investigations. Analyze observational data sets to validate and verify model results, identify model deficiencies, and identify observed phenomena that are appropriate targets for numerical model investigation.
Actively seek and apply for external support of research activities. Lead or participate in mission and spaceflight instrument proposals. Participate and contribute to Planetary Atmosphere group meetings. Present and disseminate research results at professional meetings and conferences. Submit manuscripts to peer-reviewed journals. Requirements: Requires a Ph D in Atmospheric Science, Planetary Science,
Geophysical Science, or directly related field. 2 years: Post Ph D experience with demonstrated knowledge and application of the principles of atmospheric dynamics and thermodynamics to terrestrial planetary (e.
g. Earth, Titan, Mars, Pluto, Venus, Earth-like Exoplanets) atmospheres. Expertise in the FORTRAN programming language in the Linux operating system environment. Proficiency with Python, MATLAB, IDL, or similar applications for analysis of observational data and very large model data sets. Knowledge and experience with one or more planetary atmosphere modeling codes (e. g. Planet WRF, Mars RAMS, Titan RAMS, NASA Ames GCM) or demonstrated experience with similar complex fluid dynamical codes.
Ability to debug code, test code, and implement new physics is helpful. Familiarity or interest in version control tools (e. g. Git) and parallel coding libraries MPI/MPICH. Experience or desire to contribute to space instrumentation and/or planetary mission concept development PDN-9ab994d4-106c-48c3-918f-83d466dbc214
Earth. Conduct observational data analysis that supports model validation. Incorporate additional physical schemes into numerical models. Support new and ongoing planetary modeling projects. Daily and Monthly Responsibilities: Configure, initialize, run, and analyze atmospheric numerical models of Earth, Mars, Titan, and other terrestrial planetary atmospheres.
Evaluate model results for physical consistency and plausibility; identify interesting, new, or otherwise noteworthy results; place results in the context of prior investigations. Analyze observational data sets to validate and verify model results, identify model deficiencies, and identify observed phenomena that are appropriate
targets for numerical model investigation. Present and disseminate research results at professional meetings and conferences. Submit manuscripts to peer-reviewed journals.
Participate and contribute to Planetary Atmosphere group meetings. Requirements: Requires a Ph D in Atmospheric Science, Planetary Science, Geophysical Science, or directly related field. 0 years: Academic research and a Demonstrated knowledge and application of the principles of atmospheric dynamics and thermodynamics to terrestrial planetary (e. g. Earth, Titan, Mars, Pluto, Venus, Earth-like Exoplanets) atmospheres. Expertise in the FORTRAN programming language in the Linux operating system environment. Proficiency
with Python, MATLAB, IDL, or similar applications for analysis of observational data and very large model data sets.
Knowledge and experience with one or more planetary atmosphere modeling codes (e. g. Planet WRF, Mars RAMS, Titan RAMS, NASA Ames GCM) or demonstrated experience with similar complex fluid dynamical codes. Ability and willingness to dive into model code, debug code, test code, and implement new physics is helpful. Familiarity with or willingness to learn version control tools (e. g. Git) and parallel coding libraries MPI/MPICH is helpful. PDN-9ab994dc0b-858d-6c5bd6627def
