Machine Learning and Unconventional Computing Paradigms ASPIRE Graduate Intern

Internship research opportunity (Summer 2026) for graduate students with interests in hardware-software co-design of Machine Learning (ML) algorithms and Beyond-CMOS unconventional computing paradigms. The intern will contribute to an ongoing project focused on developing and implementing ML solutions for emerging computing platforms for sensing and monitoring applications.

The intern will contribute to the hardware-software co-design of ML algorithms for unconventional computing paradigms by implementing and optimizing ML models under some hardware constraints.

Minimum Qualifications

• Must be a U.S. Citizen.

• Currently enrolled in a graduate program or higher in Computer Engineering, Computer Science, or other related disciplines.

• Must have a minimum GPA of 3.4 or higher and submit a transcript prior to hiring

Preferred Qualifications

• Strong Python programming skills

• Knowledge and experience in Machine Learning (ML)

• Experience with ML frameworks

• Experience in electronic circuit design

• Excellent written and communication skills

• Experience with using the command line (bash)

• Experience in or knowledge of optimization techniques and approaches