The University of Rochester’s Department of Computer Science seeks to hire an outstanding early-career candidate in the area of Artificial Intelligence. Appointment will be at the rank of tenure-track Assistant Professor. Specifically, we are looking to hire someone with expertise in any of the following areas: 1) Learning Theory, especially related to deep learning, 2) Machine Learning Systems (ML Ops, memory efficient training techniques, distributed model training methods with GPUs/accelerators, etc.), or 3) deep reinforcement learning. We are especially interested in applications of these areas to large language models. Exceptional candidates at the associate or full professor level, or in other AI research areas such as foundational research in natural language processing (NLP), are also encouraged to apply.

The Department of Computer Science at the University of Rochester (https://www.cs.rochester.edu/) has a distinguished history of research in artificial intelligence, human-computer interaction, systems, and theory.  We nurture a highly collaborative and interdisciplinary culture, with exceptionally strong external funding and active ties to numerous allied departments, including brain and cognitive science, electrical and computer engineering, linguistics, optics, biomedical engineering, the laboratory for laser energetics, the Goergen Institute for Data Science, the school of education, and several
departments in the medical center.

Located in Western New York State, The University of Rochester is a private, Tier I research institution and a member of the Upstate NY Higher Education Recruitment Consortium, a concerted effort to facilitate dual-career faculty hires, both within and across institutions. The greater Rochester area is a diverse, energetic, and family-friendly community located on Lake Ontario adjacent to the Finger Lakes region with a metropolitan population of 1+ million.

The University of Rochester is deeply committed to building a more diverse and representative faculty, and strongly encourages applications from groups underrepresented in computer science and higher education.  We have a vibrant Women in Computing / Minorities in Computing community, and were a charter member of the AnitaB.org BRAID Initiative, which leveraged funding from major industrial sponsors to foster diversity and inclusivity in the undergraduate program and to rigorously evaluate factors that contribute to change.

Candidates should hold a PhD degree (or equivalent) in Computer Science, and should have considerable research experience. Applicants should submit 1) a cover letter, 2) a CV, 3) a statement of research, 4) a teaching statement, 5) a statement discussing the ways in which your experiences will shape your pursuits as a member of our faculty and help you add to the University’s core values of Meliora (ever better), 6) up to three key publications (as three separate attachments), and 7) three letters of recommendation. Application materials can be found and completed on Interfolio: https://apply.interfolio.com/153917

Questions concerning this position can be addressed to Christopher Kanan (ckanan@cs.rochester.edu) or Jiebo Luo (jluo@cs.rochester.edu), co-chairs of the search committee, or Chen Ding (chen.ding@rochester.edu), chair of the Department of Computer Science. Applications must be received by January 1, 2025, to be guaranteed full consideration; submissions beyond this date risk being overlooked due to limited interview slots.

Salary: $125,000–130,000 (9-month salary)

The referenced pay range represents the full base range of pay for this job. Individual salaries will be determined within the job’s salary range and established based on market data, experience and expertise of the individual, and internal equity considerations.

The University of Rochester community is committed to Meliora – becoming ever better. Through our Vision & Values statement (https://www.rochester.edu/aboutus/values/) we affirm our commitment to equity, leadership, integrity, openness, respect and accountability.

EOE / Minorities / Females / Protected Veterans / Disabled