Assignment type: Graduation
Start date: September 2022
Assignment duration: 9 months
Educational level: WO
Desired study: Artificial Intelligence, Machine Learning, Computer Science, (Cognitive) Robotics
Language: Dutch or English
Robotic solutions are able to automatically fulfill orders without the need of a human operator. However, the state-of-the-art technology is still facing some challenges in achieving human level of performance. In this assignment you will aim to improve the stacking capabilities of a robot by implementing a learning-based approach that determines how to build the stack. This can be seen as enabling a robot to play and advanced version of the game ‘Tetris’.
In the Innovate department (part of R&D), no two days are the same. Our activities are based all over the world and each project is different. We constantly spot and explore new technologies and applications that enable Vanderlande to create valuable and sustainable systems for our customers in warehousing, parcels, and airports.
Tasks / responsibilities
- Compare different machine learning techniques and their applicability to the use case
- Design and validate a learning-based stacking algorithm
- Collaborate with Vanderlande employees to prepare a physical demonstrator
- Showcase the stacking algorithm’s performance on the demonstrator
- Knowledge of different fields of machine learning (e.g. deep reinforcement learning) and optimization techniques
- Experience with machine learning tools and platforms (e.g. Microsoft Azure, TensorFlow/PyTorch, OpenAI Gym)
- Ability to assess and expand on state-of-the-art research
- Practical experience applying machine learning on a real use case
- Strong communication skills to gather feedback from internal stakeholders
Do you recognize yourself in this challenging profile? And are you looking for an internship/graduation assignment in an organization that has been elected as “Best Employer” for years in a row? Please fill out the application form and upload your resume and cover letter. For more information, contact us by e-mail: firstname.lastname@example.org or Stef Alferink (Campus Recruiter) by phone: +31 413 755 087.