Project title: Data analysis for predictive maintenance of complex technical systems

Description:
Join our cutting-edge project to revolutionize autonomous public transport with Digital Twin Technologies and Predictive Maintenance. The transportation industry is gradually transitioning to driverless vehicles and their reliability becomes paramount. This internship focuses on analyzing sensor data from autonomous trains and developing predictive maintenance algorithms to ensure their optimal performance.
As an intern, you will work in the Cyber-Physical Systems Laboratory of the Center for Digital Engineering on:
➔ Analyzing datasets from autonomous train sensors.
➔ Developing and verifying predictive maintenance algorithms.
➔ Testing digital twin models and prediction algorithms with real train operational data.
This hands-on experience will provide you with a deep understanding of health state monitoring for complex technical systems. Ideal candidates should have a background in data analysis, machine learning, or engineering, with an interest in autonomous vehicles and digital twin technologies.
Contribute to the future of autonomous public transport by joining our team at the Cyber-Physical Systems Laboratory and driving innovation in train reliability and maintenance.

Candidates requirements:
➔ Python programming skills

English language proficiency is not required.

Supervisor: Professor of the Practice Andreas Panayi & Project Manager Tikhon Uglov

Internship duration: 1-2 months

The start date of the internship: now, latest start date March 31, 2025

Monthly compensation: With No compensation

Contact person: Andreas Panayi a.panayi@skoltech.ru

Description of the internship in Russian