POSTDOCTORAL ASSOCIATE, Materials Science and Engineering (DMSE), to join collaborative project between the group of Professor Jeffrey Grossman, Professor Yang Shao-Horn, and Dr Tian Xie. The candidate will work in a team consisting of graduate students, Ph.D.-level scientists, and faculty at the intersection of molecular simulation, machine learning, battery design, and catalysis. Responsibilities include applying and developing machine learning approaches for polymer electrolyte design; development of high throughput simulation workflows for polymer simulation; student advising/mentoring; and authoring or coauthoring technical presentations, reports and manuscripts for peer-reviewed journals and conferences.

Job Requirements

REQUIRED: Ph.D. in materials science, physical chemistry, physics, or related field; demonstrated experience and interest in development of machine learning methods for materials; experience in the computational simulation of materials; highly effective organizational, analytical, problem-solving, and presentation skills; and ability to work both individually and as part of a team.

PREFERRED: experience in the development of novel machine learning methods; experience in high throughput simulation workflows.

Interested applicants should send a cover letter and a curriculum vitae as a single PDF to Professor Jeffrey Grossman at [email protected] and Professor Yang Shao-Horn at [email protected]. Applicants should also arrange for two confidential reference letters to be sent to the same address.