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Microsoft Research AI for Science is seeking a talented 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. You can learn more about our AI emulator MatterSim and generator MatterGen in our blog.
This role is an exceptional opportunity to shape the future of materials design. You will join a highly collaborative, interdisciplinary, and diverse team of researchers and engineers to push the frontier of foundational machine learning potentials. You will have access to industrial scale compute for large-scale model training and data generation.
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.
Responsibilities
- Design and implement novel neural network architectures for training foundational machine learning potentials.
- Improve the inference speed of machine learning potentials and scale them to large systems.
- Design and implement pipelines to evaluate machine learning potentials for real-world tasks.
- Design and generate novel datasets for training foundational machine learning potentials.
- Prepare technical papers, presentations, and open-source releases of research code.
Qualifications
Required:
- PhD in machine learning, computational materials science, or related area, or comparable industry experience.
- Experience in developing machine learning force fields.
- Proficiency in collaborative code development in Python on shared codebases.
- Publication track record in relevant academic journals (npj computational materials, PRB, PRL, etc.) or top-tier conferences (NeurIPS, ICML, ICLR, etc.).
- Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds.
Preferred
- Experience in developing large scale foundational machine learning force fields.
- Experience in evaluating machine learning force fields for real-world tasks.
- Experience in designing and producing large scale datasets for machine learning force fields.
#Research #AI for Science
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
