Dear Reserachers,
Our SUNCAT team focussing on electrochemical properties of oxides recieved two seperate but related grands on hydrogen technologies from the The Hydrogen and Fuel Cell Technologies Office (HFTO), and Office of Energy Efficiency and Renewable Energy (EERE), DOE. Both projects are in direct collaboration with experimentalist, including Stanford, SLAC, Carnegie Melon and Plug Power.
Position one (still open~): DFT modeling of Low Iridium Loading Catalyst for Durable PEM Water Electrolyzers (PEMWE)
The hire will develop DFT based predictive theory to understand mixed-metal oxides and high-entropy oxides for the activity & stabilty of the anodes for Oxygen Evolution Reaction. The sucessfull candiate should have a strong motivation for independent research and ability to collaborate with experimentalist, have a PhD in relevant field withinh the last 3 years and demostarted knowlegde in computation and electronic structure calculations. We are particularly seeking the expertise in trasition-metal oxides, Pourbaix analysis, DFT with Hubbard+U, and surface based heterogenegeous catalysis, and material science.
Position two: Machine Learning for developing High-Entropy Materials as Superior Alternative Electrodes for Long-lasting Oxide-Conducting Solid Oxide Electrolysis Cells (O- SOECs)
The hire will develop machine learning model based on Crystal Graph Convolutional Neural Networks (CGCNN) and electronic strucutre descriportors such as Crystal Orbital Hamilton Population (COHP) to predict new solid-oxide-conducting solid oxide electrolysis SOEC electrode materials based on High entropy perovskite oxides (HEPOs). The sucessfull candiate should have a strong motivation for independent research and ability to collaborate with experimentalist, a PhD in relevant field withinh the last 3 years and demostarted experties in machine learning, active learnig, and the use of pyhton language and related tools. We are particularly seeking the experties in ML applied to atomistic properties and heterogenegeous catalysis and material science.
Some of the results under project one will be used to crossfeed with the second project and vice versa. Due to the DOE funding, we won't be able to accomodate the applicants on DOE's the Sensitive Foreign Nations Control list.
Please apply directly via email to me Michal Bajdich (<bajdich@stanford.edu>) and Kirsten Winther (<winther@stanford.edu>).