The extraction of transition metals from oxides is a cost-determining step in the recycling of spent lithium-ion batteries. The Urban group has been developing methods for the computational design of more energy-efficient metal-extraction processes using a combination of atomic-scale first-principles calculations, materials theory, and machine learning.
We are looking for a summer student with interest in data science for the integration of different models that we have developed and for help with the design of a web application for the interactive prediction of metal extraction. As a summer student, you will learn about the working principles of lithium-ion batteries and metal recycling, and you will employ data-science methods (Gaussian process and tree-based models) to data from atomistic first-principles calculations. You will conduct research on the integration of models for the prediction of different thermodynamic properties.
Direct Supervisor: Alexander Urban
Position dates: 6/1/2020 - 8/31/2020
Hours per Week: 35 Hours
Qualifications: This project is at the intersection of chemical engineering, materials science, and data science. Prior experience in Python programming is required, and experience in the development of web applications would be helpful.
Eligibility: SEAS only