Data-driven quantification of crystal quality in advanced semiconductor devices by electron microsco

What will you do?

During thisinternship, you'll focus on building a data analysis toolbox that combinestraditional image processing techniques with cutting-edge machine learningapproaches. Your goal? To assess the crystal quality of semiconductor devices.

TEM imaging isessential for developing these materials, but interpreting the images is thereal challenge. How can we automatically detect and quantify grain boundaries,interfaces, and defects? How do we measure interface roughness at the atomiclevel?

To tackle thesequestions, you'll explore data-driven methods that are transforming the way we analysematerials.

Your research willconcentrate on:

• Exploring quantitative image processing techniquesfor defects in 2D materials and multi-stack interfaces.
• Validating and improving existing algorithmsfor TEM image analysis.
• Testing new approaches, including unsupervisedalgorithms.
• Explore the use of automation.
• Extend the toolbox to characterizesample/stack/structure for different materials.
• Sharing your findings through reports andpublication in leading scientific journals and conferences.

Who you are?

• You hold at least a Bachelor's degree or arecurrently pursuing a Master's degree in Engineering/Science, preferably in oneof the following fields: Computer Science/Engineering, Physics, ArtificialIntelligence and Machine Learning (or any interdisciplinary course).
• You can work independently, meet deadlines, andtake ownership of your research.
• You have astrong research interest in investigating different advanced machine learningarchitectures and algorithms appropriate in the context of specified problemdomain as well as eagerness to advance the state-of-the-art.
• StrongProgramming skills (Python, matlab skill etc.).
• You are analytical, creative, and enjoy workingin a multidisciplinary environment.
• You are ateam player and have strong communication skills.
• YourEnglish is fluent, both written and spoken.

Type of internship: Master internship

Duration: 6mo - 1year

Required educational background: Computer Science, Physics, Nanoscience & Nanotechnology, Materials Engineering

University promotor: Claudia Fleischmann (KU Leuven)

Supervising scientist(s): For further information or for application, please contact Eva Grieten (< email deleted for security reasons >) and Ankit Nalin Mehta (< email deleted for security reasons >) and Paola Favia (< email deleted for security reasons >) and Andrey Orekhov (< email deleted for security reasons >)

The reference code for this position is 2026-INT-008. Mention this reference code in your application.

Only for self-supporting students.

Applications should include the following information:

• resume
• motivation
• current study

Incomplete applications will not be considered.