Berkeley Lab’s Energy Storage & Distributed Resources Division (ESDR) is looking for a Postdoctoral Fellow to join their Applied Energy Materials Group.

In this role, you will drive novel methods in computational chemistry, leveraging automated electronic structure workflows, graph theory, and machine learning to actively and strategically explore electrochemical reaction cascades with chemical reaction networks. 

What You Will Do:

• Develop methods of efficiently exploring reaction networks using information and techniques from electronic structure calculations, graph theory, and machine learning.
• Analyze complex reactive processes in energy-relevant chemical systems, e.g. charge-carrier degradation in redox-flow batteries and interphase formation in metal-ion batteries.
• Perform first-principles calculations to predict thermodynamic and kinetic properties and compile data into research databases.
• Develop codes as necessary to interface with existing Materials Project high-throughput and data storage frameworks.
• Prepare reports, research papers, and presentations, and present results at meetings.

What is Required:

• A recent Ph.D. in Physics, Chemistry, Mathematics, Materials Science, or related field.
• A strong background in thermodynamics of molecules or materials.
• Experience solving complex computational problems using tools like graph theory, tree searches, or mathematical dual spaces.
• Excellent object-oriented code development expertise.
• Excellent verbal and written communication skills.
• Strong interpersonal skills with the ability to work as an independent researcher with a high level of scientific judgment and initiative and participate in collaborative efforts as a member of a diverse research team.

What we prefer:

• Python programming experience.
• Datamining / statistical learning / deep learning experience.
• Theoretical electrochemistry experience.

To apply, please send a full CV and concise description of your expertise to Dr. Samuel Blau at [email protected].