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Microsoft Research AI for Science is seeking a talented researcher to join our mission of accelerating scientific discovery through AI. By advancing the capabilities of deep-learning-based DFT simulations through design and generation of novel training datasets with state-of-the-art quantum chemistry methods, you will help unlock new frontiers in materials science, catalysis, and beyond. Learn more about our work on accurate DFT in our blog.

This role is an exceptional opportunity to shape the use of quantum chemistry at the intersection of cutting-edge methodology and large-scale data generation, with access to world-class infrastructure. Join a highly collaborative, interdisciplinary, and diverse global team of researchers and engineers that’s redefining what’s possible in quantum chemistry.

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.

Required qualifications:

• PhD in computational chemistry, computational condensed-matter physics, or related area, or comparable industry experience.
• Strong understanding of state-of-the-art quantum chemistry methods (theory, implementations, limitations) and experience with using them.
• Proficiency in collaborative code development in Python on shared codebases.
• Publication track record in relevant academic journals (JCP, JCTC, TCA, PRB, PRL, JACS).
• Ability to work in an interdisciplinary collaborative environment, through effective communication of technical concepts to non-experts from different technical backgrounds.

Preferred qualifications:

• Experience in implementing new electronic-structure methods.
• Experience with designing and producing computational chemistry datasets.

#Research #AI for Science

• Design and generate novel datasets for training deep-learning-based DFT methods.
• In collaboration with external experts, develop scalable deployments of state-of-the-art ab-initio quantum chemistry methods.
• Develop new methods for molecular structure generation and manipulation.
• Prepare technical papers, presentations, and open-source releases of research code.