Magnet.me  -  Het slimme netwerk waar studenten en professionals hun stage of baan vinden.

Het slimme netwerk waar studenten en professionals hun stage of baan vinden.

Graduation: Integrating Component Simulations in Digital Twins with Machine Learning

Geplaatst 16 jul. 2025
Delen:
Werkervaring
0 tot 2 jaar
Full-time / part-time
Full-time
Functie
Soort opleiding
Taalvereisten
Engels (Vloeiend)
Nederlands (Vloeiend)
Startdatum
1 september 2025

Je carrière begint op Magnet.me

Maak een profiel aan en ontvang slimme aanbevelingen op basis van je gelikete vacatures.

Graduation: Integrating Component Simulations in Digital Twins with Machine Learning

Assignment type: Graduation

Start date: September 2025

Assignment Duration: 6 – 9 months

Educational Level: Master
Desired Study: Computer Science, Mathematics or any technical study with programming affinity

Language: Dutch / English

Assignment

Vanderlande uses different simulation models for different use cases. Component simulation models are highly detailed and accurate, however this level of detail makes it infeasible to integrate into large scale systems simulation, as this would become too computationally expensive and slow to be useful. Developing simpler component models requires significant effort as well as expert knowledge of component behaviour, and typically results in a model with lower accuracy. Additionally, it poses the challenge of keeping the detailed and simple models aligned over the life cycle of a product. Because of these downsides we want to investigate a different option. In this graduation assignment we want to use machine learning techniques to train a simple component model for use in system simulations, using training data from the detailed component model.

Department
The Digital Twin Suite develops the software platform used to configure system-level Digital Twins for simulation and emulation purposes. The Simulation team uses this simulation platform to configure project-specific simulation models, which they use to analyse and optimize system performance.

Tasks/responsibilities

  • Decide on case on which to apply the assignment
  • Investigate suitable machine learning techniques and model setups.
  • Train a simplified component model that is suitable for integration in large system models
  • Analyse model accuracy, reliability, computational cost and other relevant performance metrics

Skills / Your profile

  • Affinity with machine learning and programming.
  • Systems simulation models are done in Java
  • Component simulation are typically done in MATLAB
  • *Mandatory enrolment in a Dutch Education System and resident of The Netherlands

Contact

Do you recognize yourself in this challenging profile? Are you looking for an internship in our organization? For more information, contact us by e-mail: internship@vanderlande.com

Vanderlande is the global market leader for value-added logistic process automation at airports, and in the parcel market. Vanderlande’s baggage handling systems move 4.2 billion pieces of luggage around the world per year. Its systems are active in 600 airports including 14 of the world’s top 20.

Transport & Logistiek
Veghel
6.000 medewerkers