This internship focuses on building structure–property relationships for smart precursor discovery, laying the foundation for future AI projects in advanced materials research
Job Summary
This internship focuses on building structure–property relationships for smart precursor discovery, laying the foundation for future AI projects in advanced materials research.
Key Responsibilities include importing or creating molecular structures, collecting literature data, data pre-processing, descriptor calculations, and developing predictive models.
Gain hands-on experience in computational chemistry and data-driven modeling, with practical skills in Python-based data analysis and AI preparation workflows.
Matching Summary
This internship focuses on building structure–property relationships for smart precursor discovery, laying the foundation for future AI projects in advanced materials research.
Skills & Requirements
Must-have
Machine-readable chemical library
Molecular descriptors and properties
Computational chemistry workflows
Python-based data analysis
DFT calculations
Predictive models for property estimation
Nice-to-have
AI-driven materials discovery
Structure-property relationships
Industry-relevant research
Collaborative R&D teams
Key Requirements
Master’s student or PhD candidate
Experience with DFT simulations
Interest in machine learning and artificial intelligence
Hands-on experience with Python and Jupyter notebooks