Internship/Graduation: Deep Learning based stacked item segmentation
Assignment type: Internship/Graduation
Start date: As soon as possible
Assignment duration: 3 - 8 months
Educational level: Master
Desired study: Computer Science, Electrical Engineering, Mechanical Engineering, Biomedical Engineering or related
Description of assignment
For this assignment, we are looking for a smart student who will continue to improve our novel CNN based stacked item segmentation solution.
Description of department
R&D (Research & Development) at Vanderlande:
Vanderlande is a worldwide leading supplier of baggage handling systems for airports, sorting systems for Warehouse automation and Parcel and Postal services. Operating internationally means fierce competition. Continuous innovation is the key to win in a world that is changing faster and faster. Innovation is essential for Vanderlande to supply the specific solutions required by its customers. That is where the R&D department comes in, with over 200 people developing the necessary hard- and software in-house.
- Analyze the requirements of the existing problem.
- Understand the existing deep learning based solution.
- Read the latest studies.
- Improve the existing algorithm.
- Use real and artificial images to train the models.
- Compare with the state-of-the-art solutions.
- Collect new real cases to test the performance.
- Report to the rest of the team.
• Good programming skills in Python
• Good background in deep learning and CNN
• Experience with a Deep Learning framework (e.g. Keras, TensorFlow, Caffe)
Do you recognize yourself in this challenging profile? Are you looking for an internship or 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