Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
include remote sensing algorithm development, modeling studies, data fusion, sensor development, and/or snow satellite mission concept studies. Participation in the design and execution of field campaigns
-
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
-
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
-
Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://www.nasa.gov/oiir/export
-
System modeling - Statistics and data science (especially, multivariate statistics, Bayesian statistics) - Remote sensing theory (e.g., radiative transfer physics; algorithm development) - Remote sensing
-
are not limited to: Learn research techniques to develop algorithms and models for the simulation of field data Participate in experimental activities such as research design, data collection, technical
-
are not limited to: Learn research techniques to develop algorithms and models for the simulation of field data Participate in experimental activities such as research design, data collection, technical
-
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
-
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
-
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