PhD Positions in AI Foundations, Reinforcement Learning, and Interpretability at University of Southern California

Paria Rashidinejad, Assistant Professor at the University of Southern California (USC), is recruiting PhD students for Fall 2026 in the Electrical and Computer Engineering (ECE) department. Research areas include reasoning, steering, editing, and few-shot learning in foundation models (such as LLMs, diffusion language models, and vision-language architectures), reinforcement learning, AI interpretability and safety, and mathematical foundations of AI. The group is open to innovative ideas from prospective students. Applicants should have a strong background in engineering, mathematics, or hard sciences, and demonstrate research ability and experience. Required application materials include official transcripts, personal statement, resume/CV, and three letters of recommendation. The GRE is not required for most Viterbi School PhD programs, except Astronautical Engineering and Industrial & Systems Engineering. International applicants must provide English proficiency scores unless they hold a previous degree from USC. The application deadline for the USC ECE PhD program is December 15, 2025. Applications must be submitted online by 11:59 PM Pacific Time. Recommendation letters and test scores can be submitted after the application deadline, but the application itself must be complete and submitted on time. For more details and to start your application, visit the official USC Viterbi PhD application page. Funding details are not explicitly stated, but PhD positions at USC ECE typically include full tuition coverage and a stipend. Prospective students are encouraged to confirm funding specifics with the department. If you are interested in these research topics and want to discuss opportunities, you may also meet Paria Rashidinejad at NeurIPS in San Diego. Relevant academic keywords include Artificial Intelligence, Foundation Models, Reinforcement Learning, AI Safety, AI Interpretability, Mathematical Foundations, and Machine Learning. The position is ideal for students passionate about advancing the theoretical and practical aspects of AI.