Undergraduate summer internship (8-week) in ML-based optimization of high-entropy oxide materials for water electrolysis

We are seeking an undergraduate student for a summer internship, to carry out an 8-week project. The internship will take place from approximately July 7th – August 29th 2025, at SLAC National Accelerator Laboratory in Menlo Park.

The intern will collaborate with a team of SLAC and Stanford scientist to develop new materials for high temperature water splitting. The main goal is to improve the performance of solid oxide electrolysis cells (SOEC), by utilizing high-entropy electrode material, specifically A-site doped high entropy perovskite oxides. You will utilize computational modeling and machine learning to predict key material properties such as material stability, oxygen conductivity and thermal expansion coefficients.

We are seeking student currently enrolled in an undergraduate program in Chemical Engineering, Physics, Chemistry, Materials Science, or similar field. Experience with computational materials modeling such as density functional theory and machine learning is desirable. Furthermore, applicants with interest in electrochemistry and catalysis will be prioritized.

The internship covers an hourly salary as well as participation in the SUNCAT Summer Institute, taking place in the beginning of August (https://suncat.stanford.edu/events/suncat-summer-institute-2025). This is a non-benefits eligible, temporary position. Eligible applicants must be at least 18 years of age and currently enrolled in an educational program (or recently graduated) and have US work authorization. 

Please apply via email to Kirsten Winther (winther@slac.stanford.edu), including a brief motivational letter and CV.