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Senior Research Engineer in Materials – AI for Science
Job Description
Senior Research Engineer in Materials – AI for Science
Cambridge, Cambridgeshire, United Kingdom
+ 2 more locations
Date posted
Overview
This role is an exceptional opportunity to lead our ambitious data generation efforts. You will develop scalable computational workflows and create the datasets for the training of large-scale foundational models. You will work with a highly collaborative, interdisciplinary, and diverse global team of researchers and engineers to define and create the next frontier datasets for materials science.
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.
Qualifications
- PhD in computational materials science, computational chemistry, condensed matter physics, machine learning, or related area, or comparable industry experience.
- Experience in developing high-throughput DFT workflows and scaling them to tens of thousands of materials.
- 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.
- Practical experience with cloud platforms such as Azure, AWS, or Google Cloud.
- Experience in designing and producing computational materials datasets.
- Strong understanding of density functional theory and its application in simulating the electronic, magnetic, and optical properties of solid-state materials.
- Strong understanding of sampling methods (e.g., molecular dynamics, Monte Carlo methods) and their application in simulating solid-state materials.
#Research #AI for Science
Responsibilities
- Design and generate novel datasets for training deep learning models for materials design.
- Develop and deploy scalable DFT workflows for large scale data generation.
- Manage and enhance data infrastructure to support scalable and efficient data generation workflows.
- Validate the accuracy and physical correctness of DFT simulation results.
- Prepare technical papers, presentations, and open-source releases of research code.
Industry leading healthcare
Educational resources
Discounts on products and services
Savings and investments
Maternity and paternity leave
Generous time away
Giving programs
Opportunities to network and connect
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