Intern - Computational Materials Science

Internship Opportunity: Computational Materials Science Intern (Leuven, Belgium)

About the Internship

We are offering an internship opportunity for students or early-career researchers interested in computational materials science, data analytics, and AI-driven discovery. This internship focuses on building structure–property relationships for accelerated materials discovery, preparing datasets and workflows for future AI projects in advanced semiconductor research.

Key Learning Objectives:

• Learn how to build and curate a machine-readable materials library.


• Understand key descriptors and their influence on electronic and physical properties.


• Gain experience in computational materials science workflows and data-driven modeling.


• Apply Python-based data analysis and modeling techniques (Jupyter notebooks).


• Explore the integration of computational chemistry tools with data driven property prediction.

Key Responsibilities:

• Import or build bulk structures for ALD-relevant systems using online resources and internal specifications


• Collect and organize literature data for electronic and physical properties


• Data pre-processing and feature engineering/extraction


• Perform descriptor calculations and analyze correlations with target properties


• Perform DFT calculations to complement information from databases


• Develop and refine predictive models for property estimation


• Document workflows and contribute to internal knowledge base for AI projects

Preferred Qualifications:

• Master’s student or PhD candidate in Materials Science, Chemical Engineering, Electrical Engineering, Mechanical Engineering, Applied Physics, or Mathematics (with strong physics/chemistry interest).


• Experience with DFT simulations.


• Interest in machine learning and artificial intelligence


• Hands-on experience with Python and Jupyter notebooks.


• Understanding of Python ML scientific libraries and toolkits


• Strong analytical skills and interest in computational chemistry and data driven applications.

What You’ll Gain:

• Hands-on experience in computational materials science and data-driven modeling.


• Practical skills in Python-based data analysis and AI preparation workflows.


• Insight into structure–property relationships and their role in accelerated materials discovery.


• Exposure to industry-relevant research and innovation in semiconductor materials.


• Close collaboration with ASM corporate R&D and Chemistry Innovation teams.