PhD in Augmented Hybrid Engineering: Smart Data

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, 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.

WP8  Augmented Hybrid Engineering

WP8 Breakthroughs

• Scalable and fast decision making, from sensor management to real-time predictions and robust control
• Practical implementation, managing complexity, uncertainty and technical constraints

WP8 Motivations

• Pure model-based or data-based approaches cannot lead to robust and explainable decisions
• Nonlinear behavior and component interactions are hard to predict in complex system-of-systems

WP8 Innovation

• Sensor placement combined with data fusion
• Hybrid twin synthesis with faster than real-time predictions
• Correct-by-design robust control and resource efficient h/w deployments at the edge

Job Description

Develop a methodology to prepare data according to physical constraints, and to adjust physical models to real situations captured by data. The idea will be to combining imperfect data from models, from observations distributed in time and space, exploiting any relevant physical constraints, to produce a more accurate and comprehensive picture of the system as it evolves in time.

A key ingredient will be data assimilation tools and Bayesian framework to integrate physical constraints, statistical correlations and confidence metrics. Data assimilation will help to resolve dynamic state estimation, parameter adjustment, and unknown inputs identification, while Bayesian framework will allow to manage uncertainties.

Competences in some of the domains listed below will be highly considered:

• Applied mathematics
• Optimization and Control theory
• Physical modeling & Simulation-based engineering
• Constraints on real-world systems

Keywords:

1. Data science
2. Artificial Intelligence
3. Numerical Simulation
4. Engineering
5. Data Assimilation
6. Real-Time State Estimation

French CNRS scholarship = EUR2135

Workplace addresses:

1.5 years : G2Elab, bât GreEn-ER, av. Martyrs, 38000 GRENOBLE – FRANCE

1.5 years : CREATE Campus, CREATE Tower, 1 Create Way #08-01 Singapore 138602

Interested applicants please send your resume to:

Email : benoit.delinchant@grenoble-inp.fr  

– Please attach your full CV, with the names and contacts (including email addresses) of two character referees.