Overview
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 and generator 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.
Qualifications
Required:
1. PhD in machine learning, computational materials science, or related area, or comparable industry experience.
2. Experience in developing machine learning force fields.
3. Proficiency in collaborative code development in Python on shared codebases.
4. Publication track record in relevant academic journals (npj computational materials, PRB, PRL, etc.) or top-tier conferences (NeurIPS, ICML, ICLR, 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. Experience in developing large scale foundational machine learning force fields.
7. Experience in evaluating machine learning force fields for real-world tasks.
8. Experience in designing and producing large scale datasets for machine learning force fields.
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Responsibilities
9. Design and implement novel neural network architectures for training foundational machine learning potentials.
10. Improve the inference speed of machine learning potentials and scale them to large systems.
11. Design and implement pipelines to evaluate machine learning potentials for real-world tasks.
12. Design and generate novel datasets for training foundational machine learning potentials.
13. 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