-
simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
-
simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
-
: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis
Enter an email to receive alerts for model-predictive "https:" positions