Training Overview

Each aiM-NRT PhD Fellow will engage in coursework integrated with parallel professional skills development and an experiential internship with opportunities to apply aiM training. The progression through the program, the selection of professional development activities, and the identification of an internship partner will be guided by each student’s Individual Development Plan (IDP).

• Boot Camp: week-long orientation that will provide fundamental knowledge in AI/DS+ Materials Science, training in professional skills topics as well as developing communication, collaboration, and teamwork skills and getting to know team members.
• Core courses:

1. Fundamentals of Data Science for Materials Scientists  OR
2. Fundamentals of Materials Science for Data Scientists
3. Applications in Data and Materials Science (ME 555 and CS 590):  Spring 2021- Tu/Th 10:15-11:30am
4. Joint AI in Materials Capston Project Course (year long)

• Experiential Internships (3-6 Months): After two years of training in fundamentals and skills application, aiM Fellows will be prepared for an external internship. Two major Internship pathways will be offered over a 3-month summer term (most common) or a longer timeframe:

1. national laboratory
2. industrial/entrepreneurial

These collaborative internships will afford access to world-class expertise, unique experiments, and facilities to complement the skills aiM students learn through coursework and research to apply their skills to real-world problems in data-driven materials science and build their professional networks.

• An ongoing professional development program: will provide both “professional skills” and “technical skills” training all years of the program. Professional skills training will include formal and informal communication as well as K-12 outreach, mentor and mentee training, leadership and collaboration skills, and other skills critical for workforce success.
• Annual aiM-NRT Challenge Competition: will be open to student teams from across the U.S. who will submit projects that apply AI and machine learning to materials design.