Postdoc position on Digital Twin for real-time monitoring and anomaly detection of complex systems

WorkplaceLausanne, Lake Geneva region, Switzerland

The Ecole Polytechnique Fédérale de Lausanne (EPFL) is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs 6,000 people supporting the three main missions of the institutions: education, research and innovation. The EPFL campus offers an exceptional working environment at the heart of a community of 16,000 people, including over 10,000 students and 3,500 researchers from 120 different countries.
The EPFL Center for Intelligent Systems (CIS) acts as a research promotion platform for bringing together experts in machine learning, data science, computer vision, cyberphysical systems, and robotics. CIS is a joint initiative of the schools of Architecture, Civil and Environmental Engineering (ENAC), Computer and Communication Sciences (IC), Basic Sciences (SB), Engineering (STI) and Life Sciences (SV). CIS’ unique mission is to connect and support all EPFL researchers working in fields related to intelligent systems. These fields are developing technologies that, when brought together, can be used to construct intelligent systems capable of making complex, nuanced decisions in challenging, dynamic environments.

Postdoc position on Digital Twin for real-time monitoring and anomaly detection of complex systems

Your mission :
The objective is a monitoring tool for complex mechanic systems that detects precursors of potential failure mechanisms, and proposes the safest set of actions to avoid failure in real time with the minimum necessary number of probes. The idea of this project is to establish a very fast digital twin of the tested machine that captures all the relevant physical phenomena and their links by combining mathematically-based and data-driven surrogate models. The digital twin will then be used for real time assessment of new data collected during operation of the machine and their deviation from the predicted behavior.
Your mission is the development, implementation and testing of this digital twin.

Main duties and responsibilities include :

  • Build a fast mathematical output model
  • Build a classifier function
  • Experimental investigation for anomaly detection

Your profile :
The ideal candidate holds a PhD-degree in applied mathematics, or computational science and engineering or related fields, with a solid background in at least some of these areas: mechanical engineering design, optimization methodologies, model reduction and validation, uncertainty quantification, machine learning. The position requires a strong interest in developing and testing new mathematical and computational methodologies for practical applications on which experimental data are available and/or can be collected.

We offer :
The appointment will be full time for one year, renewable for the second one. The appointment will be joint between the Chair of Scientific Computing and Uncertainty Quantification (Prof. Fabio Nobile) and the Laboratory for Applied Mechanical Design (Prof. Jürg Schiffmann). The research work will be carried out in close collaboration with the EPFL Center for Intelligent Systems (CIS), with PhD candidates, engineers and master students. Since the laboratories are an international environment, excellent knowledge in written and oral English expression is required, French is a plus.

Start date :
As soon as possible

Term of employment :
Fixed-term (CDD)

Duration :
Fixed-term contract (CDD), 1 year renewable, max. 2 years in total

Remark : Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account.

In your application, please refer to and reference JobID 52443.

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