Description
The Materials Informatics Lab is hiring a researcher to develop deep learning and machine learning algorithms for predicting and designing material properties across materials, bio, and chemo informatics applications. The role focuses on advanced research areas such as graph neural networks, optimization, generation, representation learning, reinforcement learning, explainable AI, and Bayesian neural networks, with applications in bio/drug discovery, battery and OLED materials, eco-friendly catalysts, and polymer development. Candidates need a master’s degree or higher in a related field and experience researching and developing deep learning/machine learning algorithms; preferred qualifications include experience with material property prediction, structure generation, DFT tasks, and frameworks like Keras, TensorFlow, PyTorch, and Caffe, plus programming in Python, C/C++, or Java.
