IRT SystemX, DataIA convergence institute, the Labex DigiCosme, and the Systematic Paris-Region competitiveness cluster joined forces to create a new meeting, the “meet-up | PhD Candidates & the Industry “, to foster synergies and collaborations between their PhD candidates and industry players.

Aimed at doctoral students, industrialists, supervisors and thesis directors, the event brought together around a hundred participants on 14 November at Nano-INNOV (Palaiseau) around two two main objectives:

  • Keeping attendees up to date with the most current thesis work and needs felt by industrialists ;
  • Create an opportunity for meetings and exchanges to develop the work of PhD candidates or for future employment.

Doctoral students’ work in the spotlight

The thesis work of doctoral students from the IRT SystemX, the DataIA institute and the Labex DigiCosme were highlighted during a poster exhibition based on four themes:

Scientific computation and optimization

Networks and telecommunications

Software Science, System Engineering

Data Science and HMI

Throughout the day, participants voted to choose the best posters. And the two winners are…

  • Elies Gherbi
    After training in mathematics and computer science, Elies worked as a machine learning engineer. He then decided to take up the challenge and approach research as a PhD student at the IRT SystemX. His thesis topic concerns the application of artificial intelligence in autonomous transport, with the development of models capable of detecting malicious behaviour in vehicles.
  • Kevin Pasini
    Kevin studied engineering at ENSIIE with a double degree within the AIC research master in Machine learning at Paris Saclay University. He did his end-of-study internship in the MSM project (Modelling of mobility solutions) of the IRT on “Detection of anomalies in ticketing data”. He is currently working on his PhD thesis in the IVA Project (Enhanced Passenger Information). His work presented at the Meet-up concerns a Deep Learning approach for the short-term prediction of passenger flows in commuter trains.

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