Work with the machine learning team to assist in the research, applications and implementation of classical machine learning models for quantum chemistry applications. Compile and run Quantum Espresso and VASP software for generating training data for various GenMat ML and Quantum Machine Learning

(QML) models. Work with GenMat ML Research Lead to help Identify, vet and implement domain specific applications of machine learning to computational materials design including but not limited to: ANNs, RNNs, Geometric Deep Learning, VAEs, GANs and Kernel Based methods. Write python scripts to extract data from QE, VASP and other relevant sources of quantum mechanical data, transform the data using the desired feature representation and fingerprinting methods selected by the ML team, train the ML model of choice, help develop the post-processing scripts. All code developed will be expected to be end to end, with optimization and scaling issues expected to be resolved in conjunction with and primarily GenMat's DevOps/ML Ops team.

All work will be done remotely, therefore activities will include spinning up clusters and being able to run jobs on AWS or the chosen GenMat cloud platform provider using GenMat hardware. An additional option currently under development for relocation to GenMat's targeted first lab location for synthesis and fabrication will be provided in the future.

Interested in applying? Email us at hr@genmat.xyz