Responsibilities
- Collaborate with the larger Meta Reality Labs Research team to explore novel solution space for AR/VR devices
- Collaborate with the larger Fundamental AI Research team to train and utilize AI models for materials discovery and candidate material identification
- Explore & propose various approaches of first-principle simulations to understand the optical, electronic or mechanical properties of crystalline systems with complex interactions and external perturbations
- Conduct independent research and explore the broader application space given simulation results
- Contribute to and utilize internal/external tools to perform large-scale simulations
- collect & analyze results to guide further iterations to refine simulation pipelines
- Publish research results internally and contribute to solving other challenges that can be applied to Meta product development
Minimum Qualifications
- Currently has or is in the process of obtaining a Ph.D. degree in Materials Science & Engineering, Physics, Chemistry, or relevant technical fields
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
- 3+ years of experience in at least one of the following areas of research: first-principle simulations especially DFT, DFPT, GW-BSE, CCSD, for atomistic systems at scale
- 3+ years of experience studying atomistic systems with machine learning
- 3+ years of experience with programming languages such as Python, C++
- Intent to return to the degree program after the completion of the internship/co-op
Preferred Qualifications
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops and/or conferences such as APS, ACS and similar
- Experience working and communicating cross functionally in a team environment
- Experience with user-oriented productionization of AI agents/services
- Experience with large-scale materials database establishment
- Experience with training generative models to produce atomistic structures
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