Internship Opportunity: Computational Chemistry Intern (Leuven, Belgium) Summer 2026
About the Internship
We are offering an internship opportunity for students or early-career researchers interested in computational chemistry, data science, and AI-driven materials discovery. This internship focuses on building structure–property relationships for smart precursor discovery, laying the foundation for future AI projects in advanced materials research.
Key Learning Objectives:
* Build and curate a machine-readable chemical library.
* Understand key molecular descriptors and their influence on physical/chemical properties.
* Gain experience in computational chemistry 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 create molecular structures and 3D geometries using online databases
* Collect and organize literature data for relevant physical and chemical properties
* Data pre-processing and feature engineering/extraction
* Perform descriptor calculations and analyze correlations with target properties
* Run DFT calculations on select molecules
* 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 Chemical Engineering, Chemistry or related fields (Mechanical Engineering, Materials Science, Applied Physics, or Mathematics with strong 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 chemistry and data-driven modeling.
* Practical skills in Python-based data analysis and AI preparation workflows.
* Insight into structure–property relationships and their role in materials discovery.
* Exposure to industry-relevant research and innovation in semiconductor materials.
* Close collaboration with ASM corporate R&D and Chemistry Innovation teams.