Postdoc: Millimeter-Wave Cloud Radar Remote Sensing

California Institute Of Technology Jet Propulsion Laboratory
  • Remote
    30+ days ago

    Job Description

    Job Details

    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.

    Located in Pasadena, California, JPL has a campus-like environment situated on 177 acres in the foothills of the San Gabriel Mountains and offers a work environment unlike any other: we inspire passion, foster innovation, build collaboration, and reward excellence.

    The postdoc will develop and apply advanced retrieval algorithms to improve the understanding of cloud microphysical properties using ground-based, airborne, and future spaceborne radar observations. Research will include the application of Bayesian inverse methods, cloud microphysics modeling, and quantitative analysis of large atmospheric datasets to advance remote sensing capabilities and support next-generation Earth science missions. 

    The successful candidate will also participate in the analysis of observations from field campaigns, including the North American Upstream Feature-Resolving and Tropopause Uncertainty Reconnaissance Experiment (NURTURE) and Clouds And Precipitation Experiment at kennaook (CAPE-k) campaigns, and help prepare the scientific foundation for future multi-frequency cloud radar missions.

    Responsibilities

    • Conduct research in cloud microphysics and atmospheric remote sensing.

    • Develop and implement retrieval algorithms for millimeter-wave cloud radar observations using Bayesian inverse methods.

    • Analyze G-band CloudCube and other multi-frequency cloud radar datasets.

    • Process, analyze, and interpret large atmospheric and remote sensing datasets using Python and scientific computing tools.

    • Publish research results in peer-reviewed journals and present results at scientific conferences.

    • Collaborate with multidisciplinary teams to advance future radar remote sensing technologies.

    Required Qualifications

    • Ph.D. in Atmospheric Science, Earth Science, Remote Sensing, or a closely related field.

    • Research experience in atmospheric physics, cloud microphysics, or radar remote sensing.

    • Experience developing or applying remote sensing retrieval algorithms.

    • Proficiency in Python and scientific data analysis.

    • Experience working with large observational or model datasets.

    • Demonstrated record of peer-reviewed scientific publications.

     

    The appointee will carry out research in collaboration with JPL advisor, resulting in publications in the open literature.

    Applicants may be subject to additional program requirements by NASA. Postdoc positions are awarded for a minimum of one-year period and may be renewed up to a maximum of three years. Candidates should submit the following to this site: CV, representative publications, contact information for three references, and a cover letter stating their research accomplishments and interests.

    JPL has a catalog of benefits and perks that span from the traditional to the unique. This includes a variety of health, dental, vision, wellbeing, and retirement plans, paid time off, learning, rideshare, childcare, flexible schedule, parental leave and many more. Our focus is on work-life balance, and living healthy, fulfilling lives as we Dare Mighty Things Together. For benefits eligible positions, benefits are effective the first day of the month coincident with or immediately following the employee’s start date.

    For further benefits information click Benefits and Perks

    The hiring range displayed below is specifically for those who will work in or reside in the location listed. In extending an offer, Jet Propulsion Laboratory considers factors including, but not limited to, the candidate’s job related skills, experience, knowledge, and relevant education/training.

    The typical full time equivalent annual hiring range for this job in Pasadena, California.

    $83,820 - $83,820

    JPL is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, citizenship, ancestry, age, marital status, physical or mental disability, medical condition, genetic information, pregnancy or perceived pregnancy, gender, gender identity, gender expression, sexual orientation, protected military or veteran status or any other characteristic or condition protected by Federal, state or local law.

    In addition, JPL is a VEVRAA Federal Contractor.

    EEO is the Law.

    EEO is the Law Supplement

    Pay Transparency Nondiscrimination Provision

    The Jet Propulsion Laboratory is a federal facility. Due to rules imposed by NASA, JPL will not accept applications from citizens of designated countries or those born in a designated country unless they are U.S. Citizens, Legal Permanent Residents of the U.S or have other protected status under 8 U.S.C. 1324b(a)(3). The Designated Countries List is available here.

    Numbers & Facts

    Location (
    Remote
    )
    Websitehttps://www.jpl.nasa.gov/

    Skills

    • Algorithmsunmatched
    • Analysis Skillsunmatched
    • Atmospheric Sciencesunmatched
    • Bayesian Networksunmatched
    • Campaignsunmatched
    • Cloud Computingunmatched
    • Conferencesunmatched
    • Cross-Functionalunmatched
    • Data Setsunmatched
    • Earth Sciencesunmatched
    • Legalunmatched
    • Propulsionunmatched
    • Publicationsunmatched
    • Python Programming/Scripting Languageunmatched
    • Quantitative Analysisunmatched
    • Research Skillsunmatched
    • Sales Closing Skillsunmatched
    • Scientific Publicationsunmatched
    • Training Data Setsunmatched

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