Multi-fidelity reduced order modelling approach for the inverse identification of acoustic material properties (VAMOR DC10)

Job title:

Multi-fidelity reduced order modelling approach for the inverse identification of acoustic material properties (VAMOR DC10)


Job description

Offer DescriptionThis doctoral project is part of a larger, multidisciplinary and international project VAMOR: “Vibro-Acoustic Model Order Reduction” (GA 101119903) funded under the Marie-Sklodowska-Curie Actions Doctoral Networks within the Horizon Europe Programme of the European Commission.VAMOR contributes to a more sustainable and quieter future for Europe. Noise pollution has arisen as one of the key factors towards the degradation of the quality of life in European societies. In that context, efficient physics-based sound modelling is a key enabler towards not only optimized and sustainable acoustic profiles through efficient design procedures, but also affordable so-called digital twins that monitor product performance in real time. To this end, the overarching goal of VAMOR is to provide high level scientific and transferable skills training on a new generation of efficient vibro-acoustic modelling techniques, so-called model order reduction (MOR) strategies, to a group of high achieving, competent doctoral candidates to promote a quieter and more sustainable environment. VAMOR brings together a remarkable consortium, which combines research leading academic institutions – KU Leuven, Technische Universitaet Munchen (TUM), Technical University of Denmark (DTU), Kungliga Tekniska Hoegskolan (KTH), Universite du Mans, Conservatoire National des Arts et Metiers (CNAM) – with a constantly innovating, wide variety of industrial partners working on software, material, testing, design and sound enhancement (Siemens Industry Software NV, Müller BBM, Trèves, Phononic Vibes, Saint-Gobain Ecophon, Tyréns, Purifi ApS).Doctoral Candidate 10 (DC10) within VAMOR will develop novel inverse identification approaches for the characterisation of various acoustic materials by combining multi-fidelity approaches and reduced-order models. It will lead to the development of efficient machine learning models that will be used in a global optimization framework. The first step is to determine the most appropriate material models and reduced-order models for the low-fidelity and high-fidelity models with the aim of efficiently evaluating the vibro-acoustic response. The second step would be to test this approach on an established test case to estimate its robustness and the speed-up as compared to other inverse techniques. Experimental tests may be considered for the validation of the approaches and/or the enrichment of the surrogate models. Finally, the proposed methodology will be applied to more challenging inverse problem identification: stochastic inverse identification, simultaneous identification of acoustic and/or elastic and/or viscoelastic properties of foams/composites/multilayer structures, or the multilevel optimization of material parameters. Ultimately, the developed techniques will be deployed to identify the properties of degraded materials on existing structures, such as the acoustic treatment of operating vehicles.RequirementsResearch Field Engineering Education Level Master Degree or equivalentResearch Field Physics Education Level Master Degree or equivalentResearch Field Mathematics » Applied mathematics Education Level Master Degree or equivalentLanguages ENGLISH Level GoodResearch Field Engineering » Mechanical engineeringAdditional InformationBenefits

  • The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities worldwide.
  • You will receive a monthly gross salary of 2800€. The net income will result after the deduction of income tax, social contributions, and other permitted deductions that need to be considered. In addition to the net salary, you will receive a monthly mobility allowance of 420€. (Note that these amounts may vary).
  • An opportunity to pursue a joint PhD in Mechanical Engineering both from the Conservatoire National des Arts et Métiers and KTH, typically a 4-year trajectory, in a stimulating and ambitious research environment.
  • The place of employment is Paris, France. In the context of the joint degree, you will spend in total 18 months in Stockholm, Sweden. An additional 4-month secondment is included in Matelys, which is located in Lyon, France.
  • Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research groups, further doctoral training is provided in the framework of the MSCA Doctoral Network project VAMOR.

Eligibility criteria

  • I have a master’s degree in engineering, physics or mathematics and performed above average in comparison to my peers. I am not in possession of a doctoral degree at the date of recruitment.
  • I am proficient in written and spoken English.
  • I haven’t had residence or main activities in France for more than 12 months in the last 3 years.
  • During my courses or prior professional activities, I have gathered some basic experience with the basic physical principles of vibrations and/or acoustics and the related numerical modelling techniques, such as the Finite Element Method (FEM). I have a profound interest in machine learning approaches, model order reduction techniques and/or optimisation. Experience or interest in experimental testing would be appreciated.
  • As a potential PhD researcher of the Cnam, I want to perform research in a structured and scientifically sound manner, including reading technical papers, understanding the nuances between different theories and implementing and improving methodologies myself.
  • Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
  • In frequent reporting, varying between weekly to monthly, I show the results that I have obtained, and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
  • I feel comfortable to work as a team member and I am eager to share my results to inspire and be inspired by my colleagues.
  • I have a profound interest in advanced research strongly linked with industrial applications.
  • During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.

Work Location(s)Number of offers available 1 Company/Institute Le Cnam Country France City Paris Postal Code 75003 Street 2 Rue Conté GeofieldWhere to apply WebsiteContact CityParis WebsiteStreet2 rue Conté Postal Code75003 E-Mail[email protected]STATUS: EXPIRED

Expected salary

€2800 per month



Job date

Sat, 04 May 2024 22:22:29 GMT

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