ORGANISATION/COMPANYInria Saclay - Ile-de-France
RESEARCH FIELDEngineering › Aerospace engineeringEngineering › Simulation engineeringMathematics › Computational mathematics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE30/04/2017 12:00 - Europe/Athens
LOCATIONMultiple locations, see work locations below.
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/10/2017
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER722734
Computational Science aims at developing reliable and predictive numerical tools, relying on and exploiting the interactions between experiment, computation and theory. The objective is not only to numerically simulate with high-fidelity an observed phenomenon, but also to predict the reality in situations for which the numerical tool has not been specifically validated nor tested. Reliable numerical predictions require sophisticated physical models as well as a systematic and comprehensive treatment of calibration and validation procedures, including the quantification of inherent model uncertainties.
This objective is particularly difficult in the context of large scale flow simulations. This is due, on the one hand, to the high computational cost of flow simulations using complex numerical models that must account for complex nonlinear phenomenas: compressibility, discontinuities (compression shocks), turbulence, multi-scale dynamics, etc. On the other hand, the experimental measurements needed for the calibration are delicate and expensive to perform, due mainly to the unsteadiness, turbulent and multiphase nature of the flows. This limits the amount and accuracy of available measurements for the model calibration, with reduced inference quality and increased model uncertainty as a result.
Solving large scale inverse problems in complex multi-physics systems, with the inference of parameters from noisy data and in presence of model uncertainty, remains a challenging task. Typically, the calibration of a physical model compares the measurements (provided by experimentalists) to the model predictions (obtained from simulations) in a suitable and objective fashion, accounting for both data (measurements) and model (simulations) errors and uncertainties. Methods have been proposed to solve such inference problem (e.g. in Bayesian frameworks), but their computational cost is prohibitive, preventing their direct application to large scale flow inference problems.
The aim of the ESR is to develop efficient and scalable methods to solve large scale inference problems, quantify the resulting uncertainties in the physical models, and propose new experiments to optimally improve the predictive capabilities of the models. Regarding the complexity issue, we plan to combine predictions of models with different level of simplifications (multi-fidelity), according to the available computational resources, to construct a computationally manageable surrogate of the inference problem. A long-term objective will to minimize the number of numerical and physical experiments to be performed in order to calibrate the physical model with a prescribed level of confidence. These developments are also expected to provide new perspectives for the simulation-based design and optimization of complex systems.
The ESR will apply the numerical framework to the analysis of two different experiments :
the Longshot, during a 6 months secondment at von Karman Institute (Belgium). (www.vki.ac.be/index.php/research-consulting-mainmenu-107/facilities-othe... speed-wt-other-menu-158/69-mach14-free-piston-hypersonic-wind-tunnel-longshot/)
to an anti-icing system, during a 3 months secondment, at Politecnico di Milano.
- Canteen and cafeteria
- Partial coverage of the transport costs in common
- Sport equipement
- Early-Stage Researchers (ESRs) shall, at the time of recruitment by the host organisation, be in the first four years (full-time equivalent research experience) of their research careers and have not been awarded a doctoral degree.
- All researchers recruited in an ITN must be Early-Stage Researchers (ESRs) and undertake transnational mobility (including, but not limited to secondments with other UTOPIAE partner institutions, conference attendance, outreach and engagement work and any other appropriate work requiring travel as deemed necessary by their supervisor).
- Mobility Rule: at the time of recruitment by the host organisation, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organisation for more than 12 months in the 3 years immediately prior to the reference date. Compulsory national service and/or short stays such as holidays are not taken into account.
For all recruitment, the eligibility of the researcher will be determined at the time of their first recruitment in the project. The status of the researcher will not evolve over the life-time of the project.
Applicants should submit a Curriculum Vitae, a covering letter as a single document detailing the knowledge, skills and experience you think make you the right candidate for the job, two letters of reference, a list of your MSc courses and grades, copy of your Master’s thesis and preferably a list of publications.
Applicants should confirm within their covering letter the length of time they have resided in the host country in the last 3 years before the 1st of October 2017.
The ESR will be supervised by P.M. Congedo (INRIA) and Olivier Le Maître (CNRS), experts in uncertainty quantification methods (see http://www.pietrocongedo.altervista.org/ and http://perso.limsi.fr/olm/).
- Duration of contract: 36 months
- Monthly gross salary: 39,820.00 per year
- Starting date: September-November 2017
REQUIRED EDUCATION LEVELEngineering: Master Degree or equivalentMathematics: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
Candidates are required to have a Master’s degree in engineering, applied mathematics or a related discipline, and a specialization in computational fluid dynamics, uncertainty quantification, optimization or related fields. Preferable qualifications for candidates include proven research talent, an excellent command of English, and good academic writing and presentation skills.
EURAXESS offer ID: 183757