Student in Computer Science, Engineering, or Natural Sciences for Model Predictive Control

vor 2 Wochen


Stuttgart, Baden-Württemberg, Deutschland Deutsches Zentrum für Luft- und Raumfahrt (DLR) Vollzeit
Job Opportunity at Deutsches Zentrum für Luft- und Raumfahrt (DLR)

We are seeking a highly motivated and skilled student to contribute to our research team at the Institute of Vehicle Concepts in Stuttgart. As a student in computer science, engineering, or natural sciences, you will have the opportunity to work on a challenging project that involves developing and investigating control and optimization methods for virtual train platoons.

Key Responsibilities:
  • Familiarize yourself with our existing multi-train simulation model built in Python.
  • Conduct literature research on model predictive control and its implementation in similar applications.
  • Design and implement model predictive control in the multi-train simulation.
  • Evaluate the performance of model predictive control for the platooning of trains in different scenarios.
Requirements:
  • Degree in applied computer science, engineering, natural sciences, or related fields.
  • Interest in model-based development and system engineering.
  • Familiarity with Matlab/Simulink or Python.
  • Good written and spoken English and/or German skills.
  • Independent and motivated way of working.
What We Offer:
  • A dynamic and collaborative work environment at the Deutsches Zentrum für Luft- und Raumfahrt (DLR).
  • The opportunity to work on a cutting-edge project that contributes to the development of sustainable technologies.
  • Flexible working hours and occasional home office opportunities.
  • A women's network and a family-friendly work environment.
About Us:

The Deutsches Zentrum für Luft- und Raumfahrt (DLR) is a leading research center in the field of aerospace and transportation. Our mission is to advance the state-of-the-art in these fields through innovative research and development. We value diversity, respect, and equality for all individuals and promote independent work and personal growth through various training and development opportunities.



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