Machine Learning and Unconventional Computing Paradigms ASPIRE

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.

Responsibilities

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.

Qualifications

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