Postdoc Position: Plume Mapping By A Fleet Of Drones

Universities and Institutes of France
November 16, 2022
Offerd Salary:Negotiation
Working address:N/A
Contract Type:Temporary
Working Time:Full time
Working type:N/A
Job Ref.:N/A
  • Organisation/Company: CNRS
  • Research Field: Computer science Engineering Mathematics
  • Researcher Profile: First Stage Researcher (R1)
  • Application Deadline: 16/11/2022 23:59 - Europe/Brussels
  • Location: France › TOULOUSE
  • Type Of Contract: Temporary
  • Job Status: Full-time
  • Hours Per Week: 35
  • Offer Starting Date: 01/01/2023
  • - State of the art on the mapping of atmospheric phenomena with UAVs - Conception, development and validation of plume mapping processes - Participation in the supervision of trainees - Writing of research reports and scientific articles.

    Large fires cause smoke plumes that can have immediate consequences on the safety of people and property and on the environment in the longer term. Knowledge of these plumes is essential to predict their evolution and provide information on the nature of the fire.

    UAVs can collect different information within a plume (temperature, winds, nature of gases, and aerosols), but this information is only measured at the positions reached by the UAVs and is therefore spatially very scattered. The research aims to integrate the data collected in and around the plume with other available information (meteorological data, plume evolution models, possible information on the emission source, etc.) to produce a coherent global plume map. The map must allow the update of the plume evolution models, but it is also necessary for the piloting of the UAV fleet, either by operators or by algorithms, to optimize the data collection. It densely describes the plume, by explaining different parameters of interest (wind speed, gas, and aerosol concentration), and should also allow the estimation of global parameters (plume shape, position of the source).

    The problem is made difficult by the nature of the phenomenon which is volumetric and dynamic, the nature of the acquired data which are sparse and imprecise, and the variety of available information. The use of Gaussian regression processes (GPR) seems to be particularly suitable (but alternate approaches can be considered). In particular, the ways to exploit the correlations between the measured variables and the model of the phenomenon which constitutes an a priori that needs to be updated will be studied. The online estimation of the GPR hyper-parameters, and in particular their regionalization will also be considered. Realistic simulation data of plumes will be exploited, as well as some data acquired during a measurement campaign.

    The research will be done in The Robotics and InteractionS team at LAAS/CNRS, Toulouse, France (https: // www., https: // www. It will be backed by activities of the 2 years "Panache" research project, with ENAC's drone team (https: // and INERIS (https: // www. which has a reckoned experience in atmospheric plume modelling and mapping.

    Eligibility criteria

    The candidate should have (or be about to have) a Ph.D. and a good experience in stochastic estimation problems. Experience in using GPR, data assimilation techniques, and learning techniques for estimation would be a big plus.

    Web site for additional job details

    https: //

    Required Research Experiences
  • Engineering

  • None

  • Computer science

  • None

  • Mathematics

  • None

    Offer Requirements
  • Engineering: PhD or equivalent

    Computer science: PhD or equivalent

    Mathematics: PhD or equivalent

  • FRENCH: Basic

    Contact Information
  • Organisation/Company: CNRS
  • Department: Laboratoire d'analyse et d'architecture des systèmes
  • Organisation Type: Public Research Institution
  • Website: https:// www.
  • Country: France
  • City: TOULOUSE
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