Intern - Computational Materials Science
ASM · Belgium > Leuven
- On site
- Belgium > Leuven
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Subject: Interested in the Intern - Computational Materials Science role at ASM
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About the role
Step into a career with ASM, where cutting edge technology meets collaborative culture.
For over 55 years ASM has been ahead of what’s next, at the forefront of innovation and what’s technologically possible. With more than 4,500 ASMers representing 70 nationalities, our people and our advanced semiconductor devices are playing a crucial role in trends such as 5G, cloud computing, AI, and autonomous driving. But we’re more than just a tech company. We value diversity, inclusion and sustainability as we strive to make a positive impact on the world. Our development programs help support your growth, shaping your future and pushing the boundaries of innovation to unleash potential.
Internship Opportunity: Computational Materials Science Intern (Leuven, Belgium)
About the Internship
We are offering an internship opportunity for students 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.
Apply today to be part of what’s next.
We make the tech that enables the chips in devices which improve lives around the world. We do this with an eye to the future, pushing the boundaries of what’s possible through cutting-edge innovation, and driving the next wave of technological breakthroughs that shape how we live, work, and connect.
To learn more about ASM, find us at asm.com and on LinkedIn, Facebook, Instagram, X and YouTube.
ASM is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, religion, age, nationality, social or ethnic origin, sexual orientation, gender, gender identify or expression, marital status, pregnancy, political affiliation, disability, genetic information, veteran status, or any other characteristic protected by law.