A postdoctoral position is available at California State University, Northridge, focusing on the first-principles study of novel chemistry and new materials. The successful candidate will employ computer simulations based on first principles and methods such as bonding feature analyses, crystal structure searches, high-throughput calculations, molecular dynamics simulations, and machine learning techniques. These methods will be used to explore unusual chemistry and design new materials in an extended chemical space created by altered chemical and physical conditions, such as low dimensionality and high pressure.

The position is initially offered for a two-year term, with the possibility of extension for an additional 1-2 years, contingent upon research performance and mutual interest. The starting salary is $64,000 per year and will be adjusted annually for inflation. Located in the San Fernando Valley of Los Angeles, the Northridge campus is situated 20 miles from Hollywood and 35 miles from downtown Los Angeles. The candidate will have access to a recently acquired high-performance computing (HPC) cluster consisting of two 4-way NVIDIA 80GB HGX GPU nodes and 30 dual Xeon 6342 24-core CPU nodes, as well as a second cluster with 8 dual 12-core Xeon CPU nodes.

Required Qualifications and Skills:

• A Ph.D. in theoretical and computational condensed matter physics, theoretical chemistry, computational materials science, or related areas.
• Expertise in DFT-based calculation methods for solid-state materials.
• Prior experience with VASP or other solid-state computational software.
• Proficiency in analyzing electronic structures and bonding features using methods such as Bader charge analysis, COHP, and Electron Localization Functions.
• Strong capabilities in manuscript writing as well as preparing oral and poster presentations.

Additional Skills Helpful for Research Success:

• Proficiency with crystal structure search methods.
• Familiarity in conducting high-throughput DFT calculations.
• Experience in applying machine learning methods to materials science.
• Ability to construct and adjust pseudopotentials.

Motivated candidates are invited to send the following documents to Prof. Maosheng Miao at CSUN. ([email protected])

• A cover letter that briefly outlines your computational skills, research experience, and accomplishments.
• A curriculum vitae, including a list of publications.
• Arrange for 2-3 recommendation letters to be sent directly from your references.

Recent works in the lab: We utilize first-principles calculations to uncover novel chemical behaviors of matter and to design new materials with enhanced properties. Some of our recent contributions are highlighted in the articles listed below:

• A review of our recent discoveries of novel chemistry and atypical compounds stabilized by high pressure. https://www.nature.com/articles/s41570-020-0213-0
• A recent work explained the long-time puzzle of why metals adopt structures such as FCC, HCP, and BCC and why they transform into complex structures under pressure. https://www.pnas.org/doi/abs/10.1073/pnas.2218405120
• A recent work explained the chemical template effect and the mechanism that superconducting superhydride compounds might form. https://doi.org/10.1016/j.chempr.2022.10.015