Eurizon.eu

Organization: Processing Group at TELECOM Lille1

Location: Villeneuve d’Ascq, FRANCE

Field: Engineering Sciences

Requirements:

Applicants should have obtained a PhD in a relevant subject (engineering science, applied mathematics, statistics, physics), and will have experience in several of the following areas: statistical inference, inverse methods, numerical analysis, atmospheric dispersion, meteorological flow, fluid dynamics, etc… Candidates must have good mathematical and computer skills, team spirit, determination and curiosity.

Applicants should submit a letter of application explaining their suitability for the position and how they meet the selection criteria, a full curriculum vitae, list of publications, a statement of research interests and the names and contact details of two referees. Applications should be sent both to Francois Septier (francois.septier@telecom-lille1.eu) and to Patrick Armand (patrick.armand@cea.fr).

Abstract:

The threat of Chemical, Biological, Radiological and Nuclear (CBRN) attack is a frequent feature of the modern battlefield. Indeed, many rogue nations and terror groups seek to employ asymmetric warfare and some groups will be attracted by the use of chemical weapons to achieve major impact. As a consequence, rapid detection and early response to a release of a CBRN agent could dramatically reduce the extent of human exposure and minimize the cost of the subsequent clean up.

Description:

In the event of a CBRN incident, the assessment of the damage likely to be caused by the release is a problem of great importance. This assessment is usually undertaken using a predictive model for the mean transport and turbulent diffusion of the contaminant through the atmosphere, which in turn provides the information required to determine the temporal window and geographical extent of the hazard zone required in the formulation of an effective response. Unfortunately, an array of CBRN sensors by itself is not sufficient for this task, owing to the fact that detection of a toxic agent plume by the sensor array only indicates that a release has occurred, but without knowing the characteristics of the source (source location, mass, time release, agent type, etc.), the prediction of the dispersion of the contaminant in the atmosphere cannot be made.
The “reverse” estimation of source characteristics using a finite number of noisy concentration data obtained from an array of sensors has received quite a lot of attention in recent years since the importance of the solution of this problem for a number of practical applications is obvious. Nevertheless, there are still some issues for obtaining satisfactory results when the aim is to have a complete identification of unknown number of sources in real-time.
Most of the existing work is based on non-statistical methods, especially on direct-inversion procedures [1–3], where an inverse solution is obtained using an adjoint advection-diffusion equation [4]. The project aims to study whether or not the estimation accuracy could be improved by the use of statistical methods (like Monte-Carlo methods [5], Variational approaches, …). Indeed, these statistical methods could be an efficient way to deal with this challenging inverse problem. This study would be conducted by taking into account the most advanced dispersion model, the so-called Lagrangian particle dispersion model. In this model, thousands of individual particles (fluid elements) are traced and their distribution yields an estimate for the concentration field [6,7]. The particle dispersion model is the most appropriate when one want to take into account possible inhomogenities in the flow and turbulence fields [8], at the expense of high computational time. As a consequence, both complexity and performances of these inverse methods should be analyzed in order to study their feasibility.
 
This funded position will take place in the Signal Processing Group at TELECOM Lille1 (rue marconi 59653 Villeneuve d’Ascq, FRANCE -    http://www.telecom-lille1.eu/people/splab/).
This position will last one year from march 2011 with a strong collaboration with the CEA/DAM (http://www.cea.fr/english_portal) as well as the University of Troyes (Team LM2S/UTT :  http://icd.utt.fr/en/teams/lm2s.html)

Deadline: 10-03-2011

Contacts:

Email: francois.septier@telecom-lille1.eu

Email: patrick.armand@cea.fr

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