Research Fellow in Advanced Simulation in Structural Mechanics – DesCartes

at  CNRSCREATE LTD

DESCARTES PROGRAM

The DesCartes programme is developing a hybrid AI, combining Learning, Knowledge and Reasoning, which has good properties (need for less resources and data, security, robustness, fairness, respect for privacy, ethics), and demonstrated on industrial applications of the smart city (digital energy, monitoring of structures, air traffic control).
The program brings together 80 permanent researchers (half from France, half from Singapore), with the support of large industrial groups (Thales SG, EDF SG, ESI group, CETIM Matcor, Naval Group, ARIA etc.).
The research will take place mainly in Singapore, at the premises of CNRS@CREATE, with a competitive salary and generous funding for missions.
Read more about the DesCartes program here.

DESCRIPTION

The research work concerns the remote sensing demonstrator developed in the context of the DesCartes project on the use of hybrid artificial intelligence for monitoring, diagnostic and prognostic in structural mechanics in strong connection with the industrial partners: Naval Group and CETIM.
From the already performed researches in the context of Descartes involving inverse identification by data assimilation (images and acoustic spectra) for localizing localized damages (with their sizes and severities) at the laboratory scale, the present position, aims at gaining maturity to prove the validity at the semi-industrial level.
The technologies will apply on pressurized reservoirs, composites and metallic, with data and monitoring provided by the industrial partners, Naval Group and CETIM respectively. Damages, delamination in composites and corrosion in the metallic case, will be identified by using mechanical models (with the reduced model counterparts) ensuring the learning, smart-sensing by locating the adequate sensors (static and piezo-electric) in the optimal location (while minimizing the number of sensors), the real-time data assimilation and the efficient solution (by using hybrid AI) of the associated inverse problems. The integration of all these technologies will constitute the reservoir digital twin.

EXPERIENCE & QUALIFICATIONS

Minimum:
– PhD defended with post-doctoral experience in the domain of advanced simulation in structural mechanics
– Background and research experience in mechanical engineering and computational mechanics
– Experience on mechanical modelling and simulation

Keywords:

• Structural mechanics
• Machine learning
• Inverse methods
• Finite elements

Please refer the Job description for details