Student Assistant in the Field of Laser Functionalization of Thin Films (all genders)

Fraunhofer-GesellschaftAachen, North Rhine-WestphaliaOn-siteInternshipListed 8 hours ago

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About this role

At the Fraunhofer Institute for Laser Technology ILT, we may not be developing lightsabers to fight the dark side of the Force, but our innovations often sound like something out of science fiction. That’s because we’re shaping the technologies and processes for the future of digital manufacturing, packaging and scanning technology, and many other laser-based applications—such as 3D printing and laser fusion.

In the Thin Film Processing group, we focus on developing processes for the laser-based functionalization of thin films. Functionalization can involve, for example, drying, sintering, melting, or crystallization to adjust the film’s conductivity, adhesion, impermeability, or other properties.

One focus of our work is printed electronics (e.g., for strain gauges and temperature sensors). We also produce the layers to be processed ourselves using digital printing methods.
Another key area is process development for new, environmentally friendly anti-adhesive coatings (PFAS-free).

If you’d like to be part of an innovative team and are interested in helping shape the technologies of tomorrow, then you’ve come to the right place! The position is based in Aachen—take this opportunity to contribute your ideas and work with us on groundbreaking solutions.

Be part of change

- As part of your student assistant role, you will assist with the preparation and conduct of experiments

- In the chemistry lab, you will carefully prepare samples and assist with other lab tasks as needed

- After receiving appropriate training, you will work with Class 4 lasers and multi-axis systems

- You will assist with sample preparation using inkjet printers for metallic nanoparticles and dispensers for polymer insulation layers

- When analyzing samples, you will use techniques such as light microscopy, profilometry, 4-point measurements, and cross-section analyses, and document the results using Excel, Python, Origin, or PowerPoint