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Microsoft Research AI for Science is seeking a talented research engineer to join our mission of accelerating scientific discovery through AI. In the materials team, we are building next generation foundational AI capabilities to accelerate the design of novel materials. This role is an exceptional opportunity to lead our ambitious data generation efforts. You will develop scalable computational workflows and create the datasets for the training of large-scale foundational models. You will work with a highly collaborative, interdisciplinary, and diverse global team of researchers and engineers to define and create the next frontier datasets for materials science.
Job Responsibility:
Design and generate novel datasets for training deep learning models for materials design
Develop and deploy scalable DFT workflows for large scale data generation
Manage and enhance data infrastructure to support scalable and efficient data generation workflows
Validate the accuracy and physical correctness of DFT simulation results
Prepare technical papers, presentations, and open-source releases of research code
Requirements:
PhD in computational materials science, computational chemistry, condensed matter physics, machine learning, or related area, or comparable industry experience
Experience in developing high-throughput DFT workflows and scaling them to tens of thousands of materials
Proficiency in collaborative code development in Python on shared codebases
Publication track record in relevant academic journals (npj computational materials, Nature Materials, PRB, PRL, etc.)
Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds
Nice to have:
Practical experience with cloud platforms such as Azure, AWS, or Google Cloud
Experience in designing and producing computational materials datasets
Strong understanding of density functional theory and its application in simulating solid-state materials
Strong understanding of sampling methods (e.g., molecular dynamics, Monte Carlo methods) and their application in simulating solid-state materials