Overview
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. You can learn more about our AI emulator and generator in our blog.
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.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more, and we’re dedicated to this mission across every aspect of our company. Our culture is centered on embracing a growth mindset and encouraging teams and leaders to bring their best each day. Join us and help shape the future of the world.
This post will be open until the position is filled.
Qualifications
Required:
1. PhD in computational materials science, computational chemistry, condensed matter physics, machine learning, or related area, or comparable industry experience.
2. Experience in developing high-throughput DFT workflows and scaling them to tens of thousands of materials.
3. Proficiency in collaborative code development in Python on shared codebases.
4. Publication track record in relevant academic journals (npj computational materials, Nature Materials, PRB, PRL, etc.).
5. Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds.
Preferred:
6. Practical experience with cloud platforms such as Azure, AWS, or Google Cloud.
7. Experience in designing and producing computational materials datasets.
8. Strong understanding of density functional theory and its application in simulating the electronic, magnetic, and optical properties of solid-state materials.
9. Strong understanding of sampling methods (e.g., molecular dynamics, Monte Carlo methods) and their application in simulating solid-state materials.
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Responsibilities
10. Design and generate novel datasets for training deep learning models for materials design.
11. Develop and deploy scalable DFT workflows for large scale data generation.
12. Manage and enhance data infrastructure to support scalable and efficient data generation workflows.
13. Validate the accuracy and physical correctness of DFT simulation results.
14. Prepare technical papers, presentations, and open-source releases of research code.
Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.Industry leading healthcareEducational resourcesDiscounts on products and servicesSavings and investmentsMaternity and paternity leaveGenerous time awayGiving programsOpportunities to network and connect