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fellow to assist with implementing science algorithms for processing imaging spectroscopy data from its raw state (Level 0) through to global gridded data products (Level 3).** The fellow will also have
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retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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of all-sky microwave radiance assimilation algorithm are highly encouraged. Field of Science: Earth Science Advisors: Zhu, Yanqui (301) 614-5844 yanqiu.zhu@nasa.gov Applications with citizens from
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systems. The candidate will be responsible for processing repeat pass inSAR data and implementing efficient data calibration algorithms based on heterogeneous spatial sampling of ground truth points
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; willing to work with preexisting scientific algorithm; able to work independently, but in coordination with a broader science team; interested in working at the interface between fundamental research and
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developing light curve detrending and transit search algorithms. For each of these areas, we anticipate opportunities to work with current data and to obtain new observations of transiting exoplanets with
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System modeling - Statistics and data science (especially, multivariate statistics, Bayesian statistics) - Remote sensing theory (e.g., radiative transfer physics; algorithm development) - Remote sensing
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sensing for aero-optic applications. The goal is to advance the wavefront sensing capability and include TIP/ILT estimates in the algorithm. Why should I apply? Under the guidance of a mentor, you will
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sensing for aero-optic applications. The goal is to advance the wavefront sensing capability and include TIP/ILT estimates in the algorithm. Why should I apply? Under the guidance of a mentor, you will