Do you enjoy hands-on research, figuring out how things really work, and pushing technology past its limits? Then we are looking for you!
In this internship, you’ll dive into the fascinating world of jetting high viscosity materials, combining physics, engineering, materials science, and increasingly, AI assisted analysis.
You will explore how viscous fluids behave when jetted, what makes them stable or unstable. Expect a mix of theory, practical lab work, and the chance to use AI tools to support data analysis and pattern detection.
What you’ll do during this project
1. Literature & Technology Exploration
• Review current research on high viscosity jetting
• Identify experimental methods, measurement tools, and emerging AI based modelling techniques
2. Hands On Learning
• Receive training on operating our high viscosity jetting setup
• Get familiar with complementary tools, including options for AI assisted pictures analysis or data interpretation
3. Experimental Design & Execution
• Design experiments that test jet stability and droplet formation
• Run tests, capture data, and troubleshoot practical challenges
4. Data Interpretation & Insight Generation
• Analyse jetting behaviour across different flow regimes
• Compare your findings with theory and published research
• Optionally: apply AI supported analysis (e.g., image recognition for droplet tracking, automated classification of jet stability)
5. Communicate Your Results
• Write a clear, scientific report (2,000–3,000 words)
• Present your findings in a brief, engaging presentation
What you will learn
By the end of this internship, you will be able to:
• Understand and explain the fluid dynamics behind high viscosity jetting
• Recognize the key parameters influencing jet formation and breakup
• Apply experimental and analytical methods used in advanced printing and jetting research
• Work with AI as a support tool for data exploration and pattern discovery
• Communicate research in both written and visual formats
Who you are
You are a third- or fourth‑year bachelor’s student in Physics, Applied Science, Engineering, or a related field. You are naturally curious, think analytically, and enjoy taking on challenges. In addition, you feel comfortable working independently and taking full ownership of your project.
What’s in it for you?
• A truly hands on research assignment with experienced coaching
• Internship or graduation allowance up to €500 per month
• Travel cost reimbursement (if you don’t have an OV week card)
• The chance to collaborate with experts across several disciplines
• A work environment built on collaboration, innovation and ownership, where you’re encouraged to experiment and bring fresh ideas
• Exposure to how AI can support real world R&D work, giving you skills that are in high demand
Interested?
Are you interested in this assignment? Please click on the button 'apply now' where you can upload your resume and motivation letter.
If you would like to receive more information concerning this assignment, please contact Kateryna Filippovych, kateryna.filippovych@cpp.canon. If you have any questions about the internship in general, please contact careers@cpp.canon
Waar staan wij voor?
Wij ontwikkelen en produceren hightech printproducten en workflow software voor de commerciële printing markt als onderdeel van Canon, wereldwijd marktleider in beeldtechnologieën. Met ongeveer 3300 werknemers verspreid over drie continenten en ons hoofdkantoor in Venlo, Nederland, innoveren we om hoogwaardige oplossingen te creëren die kleur toevoegen aan de wereld. Geleid door de Kyosei filosofie - samen leven en werken voor het algemeen belang – is onze cultuur gebouwd op openheid, collegialiteit, vertrouwen en stabiliteit. We stellen onze mensen in staat om te groeien, initiatief te nemen en impact te maken.
Met onze bedrijfswaarden samenwerking, innovatie en eigenaarschap, streven we ernaar het beste in elkaar naar boven te halen, grenzen te verleggen en ons gesterkt te voelen om eigenaarschap te nemen in ons werk.
Meer informatie over Canon Production Printing is beschikbaar op cpp.canon.
#LI-DNI