Jobs offer

stage-LAB01.pdf
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Master thesis / internship

Several offers for Master/engineer students in :

We can also hire students in master 1 for internships of 5 months or more.

PhD Position (F/M): Data assimilation from stochastic reduced order models In turbulent fluid dynamicsThis PhD thesis is funded by the ANR project RedLUM, in collaboration with Inria Bordeaux (Angelo Iollo, Tommaso Taddei), Sant’Anna School of Advanced Studies (Giovanni Stabile), and the companies Scalian DS and Weather Measures. The student will collaborate with the other partners of the team to ultimately devise an efficient simulation framework for agricultural frost control involving wind machines at an agricultural plot scale. Keywords: model order reduction; data assimilation; observation model; stochastic To monitor and control fluid system, we seek to estimate the air flow around system, though in fluid mechanics, simulations are generally very expensive in terms of calculation time. To tackle real-time applications, it is necessary to deduce from a data set a reduced-dimensional model, which approximates the original PDE in a specific application framework. Despite the many recent advances, rapid and reliable approximation of parametric high-Reynolds turbulent flows remains an outstanding task for state-of-the-art model reduction techniques. First, due to the slow decay of the Kolmogorov n-width, projection-based (Galerkin or Petrov-Galerkin) reduced-order models (PROMs) based on proper orthogonal decomposition (POD) need to cope with significantly under-resolved representations of the solution field, which prevent accurate approximations of the full system dynamics. Second, the chaotic nature of the system challenges the predictive abilities of state-of-the-art projection methods. To address these issues, several authors have proposed stochastic closures of PROMs: the distinctive feature of these approaches is to approximate the deterministic chaotic dynamics through a system of stochastic ordinary differential equations for the dominant POD modes; the prediction of the solution is then obtained through a PROM ensemble forecast. Eventually, these simulations are coupled  to a measurement stream (data assimilation) to correct them on-the-fly. The aim of the project is to develop, analyze, validate and compare reduced data assimilation technique for 3D incompressible turbulent flows. The point of departure is the stochastic closure modeling procedure and the existing code coupled with the C++ solver Ithaca FV. The PhD student will acquire a broad vision of the data assimilation process and its limitation: from the theory, to the implementation, the data qualification and the experiments. Both synthetic and experimental data will be considered. We shall also consider the efficient treatment of unknown turbulent inflow conditions and the development of efficient hyper-reduction techniques to handle nonlinear terms.

PhD

PhD offers related to Stochastic reduced order models for real-time data assimilation

https://sites.google.com/view/valentinresseguier/projects#h.cay7n9kw0blz

LAB-PHR-CS-1905-27-computer-sciences.pdf

Permanent position

The LAB (SCALIAN DS R&D departement) is looking for a PhD holder in computer sciences, signal processing or applied mathematics with skills in HPC, numerical analysis, and possibly machine learning.