Research Fellow (AI for Materials Discovery)
Core
Develop AI-driven and data-driven approaches for the discovery and design of functional materials for energy storage, energy conversion, and electronic applications.
Role type
Research Fellow (AI for Materials Discovery)
Builds
Production ML models | research
Domain
Materials Science + Artificial Intelligence
Deliverable
research
Required skills
Machine learning, deep learning, first-principles calculations (DFT), molecular dynamics, high-throughput computational screening, scientific programming, materials simulation software
Preferred skills
Graph neural networks, generative models, transfer learning, materials informatics, physics-informed machine learning
Technologies
DFT, MD, graph neural networks, generative models
Responsibilities
Develop and apply machine learning and deep learning models for materials property prediction and inverse design; Perform high-throughput first-principles (DFT) and molecular dynamics (MD) simulations; Build and curate materials datasets, workflows, and platforms; Collaborate with experimental groups to guide materials selection; Investigate structure-property relationships in functional materials; Develop interpretable and physics-informed AI approaches; Mentor junior researchers and support laboratory management
Seniority
Mid-Senior, hands-on IC researcher