Postdoc in AI-Driven Materials Discovery
Core
Developing autonomous AI and machine-learning frameworks for accelerated discovery of multicomponent optoelectronic materials for energy-conversion applications.
Role type
Postdoctoral researcher in AI-driven materials discovery
Builds
Autonomous materials-discovery framework, generative AI models, and open scientific software
Domain
Physics, Chemistry, Materials Science, Data Science, Sustainable Energy
Deliverable
production ML models
Required skills
Machine learning, scientific computing, computational physics, computational chemistry, Python programming, scientific software development
Preferred skills
Deep learning frameworks (PyTorch, TensorFlow), atomistic simulations, density functional theory (DFT), molecular simulations, machine-learning potentials, generative AI, active learning, uncertainty quantification, Bayesian optimization, reinforcement learning, high-performance computing (HPC), student supervision
Technologies
PyTorch, TensorFlow, Python
Responsibilities
Develop machine-learning and AI methods for materials discovery; Design and implement active-learning workflows; Develop and evaluate generative AI models for inverse materials design; Perform large-scale computational screening using first-principles calculations; Analyze structure–property relationships; Publish research results in leading international journals; Supervise master's and PhD students; Contribute to teaching at undergraduate and master's levels
Seniority
Postdoctoral researcher, independent IC with teaching responsibilities