Research Scientist - Scientific Foundation Model

About the Company

Materials Intelligence Lab is a research organization that develops next-generation new materials using AI. We aim to rapidly explore and design new materials by combining machine learning and simulation.

Responsibilities

• Develop Materials Foundation Models capable of handling organic and inorganic materials integrally
• Build and preprocess large-scale material datasets
• Research and apply latest architectures such as Graph Neural Networks (GNN) and Transformers
• Build and operate large-scale training pipelines
• Analyze and implement the latest AI and Scientific Machine Learning papers
• Perform model performance evaluation and benchmarking
• Write papers for international conferences and journals

Requirements

• Master's or PhD degree (or expected graduate) in a related field
• Experience in Machine Learning research or development
• Proficiency with deep learning frameworks such as PyTorch or JAX
• Ability to read and implement the latest machine learning papers
• Understanding of Deep Learning, Representation Learning, and Graph Learning
• Ability to work full-time on a 1-year contract

Preferred Qualifications

• Research experience in Scientific Machine Learning, Geometric Deep Learning, or Foundation Models
• Research experience with GNN, Transformer, or Equivariant Models
• Experience processing large-scale graph data
• Experience using latest MLIP frameworks like NequIP, MACE, MatterSim, CHGNet, or SevenNet
• Publication record in major conferences (ICML, NeurIPS, ICLR, AAAI) or domain journals