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postdoc, Lille
Du 1 janvier 2023 au 31 août 2024
Contact : M. Meldi (marcello.meldi@ensam.eu)
The work of the candidate will aim for the development of the following tasks: 1. Implementation of data streaming machine learning techniques within the C++ software developed by the research group, aiming to reconstruct an augmented IBM formalism. The AI learning procedure will be fed by online data generated by the MGEnKF. 2. The performance of the code will be assessed via the analysis of progressively more complex scale-resolved turbulent flows. The test cases investigated include the turbulent channel flow and, in case of success, complex test cases such as a radial pump will be investigated. For every test case considered, high fidelity DNS / experimental data are already available from previous analyses of the research group.
The research group has recently developed a software able to perform online sequential data assimilation exploiting a reduced-order technique referred to as multigrid Ensemble Kalman Filter (MGEnKF). Following some works recently proposed in the literature, data stream learning will be integrated within the code to derive new paradigms for data-augmented IBM.
Objectives: the work of the candidate will aim for the development of the following tasks:
1. Implementation of data streaming machine learning techniques within the C++ software developed by the research group, aiming to reconstruct an augmented IBM formalism. The AI learning procedure will be fed by online data generated by the MGEnKF.
2. The performance of the code will be assessed via the analysis of progressively more complex scale-resolved turbulent flows. The test cases investigated include the turbulent channel flow and, in case of success, complex test cases such as a radial pump will be investigated. For every test case considered, high fidelity DNS / experimental data are already available from previous analyses of the research group.
Host Institution: Arts et Métiers - LMFL. The candidate will be based in Lille (France).
Scientific Leader: M. Meldi (marcello.meldi@ensam.eu)
Candidate’s profile: the candidate must have strong competences in machine learning techniques, ideally for applications with streaming data. A PhD degree in this area of expertise is required. In addition, skills in the numerical simulation of turbulent flows and / or IBM tools and / or OpenFOAM are welcome, but they are not mandatory.
Duration of the contract and start date: the proposed contract is for a duration of 18 months with a scheduled start date for January 2023. Some flexibility for the start date can be granted depending on the availability of the candidate.
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- postdoc-iwp-ibm-da_1662384331785.pdf (PDF, 119 Ko)