Role overview
Microsoft Research AI for Science is seeking a talented applied scientist 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 MatterSim and generator MatterGen in our blog.
This role is an exceptional opportunity to bring our foundational AI capabilities towards real-world impact. You will work closely with experimental partners and apply our foundational AI models towards solving materials design problems. You will work with a highly collaborative, interdisciplinary, and diverse global team of researchers and engineers to bridge the gap between computation and experiments.
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.
Responsibilities
- Work closely with experimental partners and translate foundational AI capabilities towards real-world impact.
- Develop computational workflows to simulate properties relevant to the experimental partners.
- Collaborate with experimental partners to validate computationally designed materials candidates in laboratories.
- Learn to interpret experimental data and translate them to actionable insights for model development.
- Prepare technical papers, presentations, and open-source releases of research code.
- PhD in computational materials science, condensed matter physics, or related area, or comparable industry experience.
- Experience in developing computational workflows to solve problems relevant to experimental materials discovery.
- 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.
Preferred qualifications
- Strong expertise in developing density functional theory workflows for solving real-world experimental problems.
- Experience in deeply collaborating with experimental groups and applying computational materials design approaches to discover novel materials.