Submission #401

Submission information
Submitted by Anonyme (not verified)
Thu, 06/06/2019 - 16:52
82.64.50.194
Yes
Industry
Postdoctoral position in Bayesian inverse algorithms
Maths
Short Term
ONERA
http://www.onera.fr
sidonie.lefebvre@onera.fr
Palaiseau
France
Postdoctoral position at ONERA- The French Aerospace Lab. ONERA's missions:
Developing and guiding research activities in the aerospace field
Disseminating, in collaboration with the authorities or organisations responsible for scientific and technical research, the results of said research at national and international levels; promoting their use by the aerospace industry and, where appropriate, facilitating their application outside the aerospace field..

Contex of the postdoc: The global warming concerns imply the study of atmospheric aerosols, including soot aggregates emitted by aircraft engines. Particulate matter emitted by aircraft remains in the upper troposphere and lower stratosphere and affects the global radiative budget: ice condensates around soot aggregates nuclei to form contrails. Besides, soot is a factor of health damage by entering the lung cells. Real-time monitoring and characterization of soot aggregates required accurate determination of soot particle size and morphology.
One goal of our research project is to address the need of active remote-sensing of fine particulate matter emitted from aircraft engine. LiDAR (LIght Detection And Ranging) is an active remote sensing technique for measuring the backscattered light from particles or molecules in the atmosphere. Inversion of the lidar signal is a well-known ill-posed problem.

The objective of this postdoc is to develop an inversion algorithm for lidar signals with high spatial and temporal resolution, especially in the frame of short-range lidar measurements. Bayesian inversion (such as optimal estimation) seems to be well suited for our problem with some adaptations. As a matter of fact, the distribution of noise is not necessarily Gaussian for low signal-to-noise ratio observations, and optimal estimation cannot be directly applied in this case. Moreover, in order to account for large amount of temporal data, it can be interesting to take advantage of machine learning methods instead of performing repeated calls to a complex forward model. Some ideas in this direction have been proposed very recently, such as using quantile regression neural networks to estimate the a posteriori distribution of remote sensing retrievals.

The candidate will be in charge of the following activities:
1) Providing a state-of-the-art about lidar inverse and optimal estimation algorithms.
2) Developing retrieval algorithms for both synthetic lidar signal and measured lidar signals from metrological validation campaigns
3) Dissemination of the results and publications

net yearly salary: 25 keuro
Euro
pdoc-dota-2019-02.pdf
Tue, 12/31/2019