Intern - Computational Chemistry

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.