|From||Dmitry Efremenko <Dmitry.Efremenko@dlr.de>|
|Date||Wed, 28 Mar 2018 17:00:28 +0200|
DLR – DAAD – Fellowships
Fellowship No. 325
Research Topic: Fast retrieval of trace gases and cloud parameters from satellite measurements of high spatial resolution
DLR Institute: Remote Sensing Technology Institute (IMF) at DLR Weßling/Oberpfaffenhofen
Position: Doctoral Fellow
The institute's Atmospheric Processors section conducts research on atmospheric remote sensing with infrared and ultraviolet spectrometers. Research on the mathematical and physical basics of atmospheric remote sensing is focused on radiative transfer modeling, mathematics of inversion, and electromagnetic scattering. The new generation of hyper-spectral atmospheric composition sensors has unprecedented high spatial resolution. For the simulation of satellite measurements from instruments with high spatial resolution, it is important to account for the sub-pixel cloud inhomogeneity, or at the least, to assess this effect on the radiances at the top of the atmosphere, and so, on the retrieval results. This PhD-project will be focused on the development of algorithms for retrieval of trace gases and cloud properties which take into account inhomogeneity in the field of view of the instrument. The newly developed methods will be applied to the next generation of satellite sensors, for example, the European missions Sentinel-5 Precursor, Sentinel-4 and Sentinel-5.
We are striving to increase the proportion of female employees and therefore particularly welcome applications from women. Disabled applicants with equivalent qualifications will be given preferential treatment.
Master in physics, mathematics, computer science, or in a similar field. Good programming skills in a Unix/Linux environment. The candidate should have some background in atmospheric remote sensing or radiative transfer. Open communication, team spirit and a can-do attitude are expected.
Knowledge and experience in Monte-Carlo simulations, remote sensing measurements, retrieval techniques, stochastic cloud models.
Fluent in spoken and written English (see requirements on www.daad.de/dlr)
Earliest Start Date: July 2018
Application Deadline: until position filled
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