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Microsoft Research AI for Science is seeking a talented machine learning researcher 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 with industrial impact. You can learn more about our AI emulator MatterSim and generator MatterGen in our blog. This role is an exceptional opportunity to drive our ambitious research agenda, develop efficient and expressive machine learning models for materials, and translate this research into real-world impact. You will work with a highly collaborative, interdisciplinary and diverse team of researchers, engineers and scientists to develop and implement the next generation of machine learning models for materials design. 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 materials design. This post will be open until the position is filled. A completed or nearly completed PhD or comparable industry research experience is expected.
Job Responsibility:
Contribute to and drive an ambitious, high-impact, research agenda on machine learning for materials
Develop efficient and expressive machine learning models that address fundamental materials science problems
Work with domain experts to develop realistic machine learning metrics and benchmarks
Prepare technical papers and presentations
Requirements:
PhD in computer science, machine learning, computational materials science or related area, or comparable industry experience
Track record of publications at top-tier conferences or journals (e.g., NeurIPS, ICML, ICLR, Nature/Science or relevant sub-journals)
Strong coding ability and proficiency in collaborative code development
Ability to quickly iterate between ideation, implementation and evaluation of new research ideas
Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds
Nice to have:
Experience in LLMs, agentic frameworks and/or reinforcement learning
Experience working on (materials) science problems
Experience with agent-driven research and code development