AI-Accelerated Materials Design Intern

Position Summary

As a Computational Materials Discovery Intern, you will work at the intersection of materials science, computational physics, quantum chemistry, quantum computing, and AI / ML. You will contribute to simulation workflows, property prediction pipelines, materials dataset generation, and hybrid quantum–classical algorithms for accelerated materials design—integrated with QpiAI’s quantum computing systems and AI platforms. This role is ideal for candidates who want to work on real scientific problems involving electronic structure modeling, materials screening, generative AI for materials, and quantum-accelerated materials simulation.

Key Responsibilities

1. Materials Simulation & Electronic Structure Modeling

• Perform classical materials simulations using :
• DFT (plane-wave & localized basis set methods)
• Pseudopotentials, k-point sampling, band structure and DOS calculations
• Phonon calculations, elastic constants, mechanical / thermal property prediction
• Molecular dynamics (MD) and Monte Carlo sampling
• Execute materials workflows for :
• Crystal optimization
• Defect formation energies
• Adsorption energies
• Reaction pathways (NEB)
• Phase stability diagrams
• Surface modeling (slabs)
• Catalyst descriptors (d-band center, charge transfer etc.)
• Benchmark performance across classical solvers (VASP, Quantum ESPRESSO, CP2K, GPAW, CASTEP, LAMMPS).

2. Materials Informatics & AI / ML for Materials

3. Quantum Computing for Materials Simulation

4. Simulation Workflow Engineering

5. Research, Experimentation & Documentation

Required Skills

Technical Skills

Domain Knowledge

Soft Skills

Preferred Qualifications