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PhD Fellowship 2016

PhD Research Fellowships in Artificial Intelligence and Machine Learning at UC Irvine

The Computer Science department at UC Irvine (UCI) is seeking applicants for PhD research fellowships in artificial intelligence, machine learning, and their related applications, including topics such as deep learning, statistical learning, graphical models, information extraction, computer vision, high-dimensional data analysis, and more.

We have an active and growing cohort of over 30 PhD students working on these topics advised by well-known faculty such as Anima Anandkumar, Pierre Baldi, Rina Dechter, Charless Fowlkes, Alex Ihler, Rick Lathrop, Eric Mjolsness, Padhraic Smyth, and Xiaohui Xie, as well as recent new additions to the faculty such as Sameer Singh and Erik Sudderth.

Students in our PhD program are fully funded throughout their studies via a combination of fellowships, teaching assistantships, and graduate research fellowships. We have recently received a large National Science Foundation training grant providing an additional 20 PhD fellowships with exciting opportunities for students to work on new machine learning theories and algorithms for problems in the natural sciences, in areas ranging from particle physics, to chemistry, to earth and climate science, to astronomy.

The UCI Data Science Initiative is also fueling growth of AI and machine learning opportunities for PhD students on campus, with a large variety of collaborative research opportunities with departments on campus such as cognitive science, economics, neuroscience, informatics, and more.

PhD applications are due by December 15th
(although late applicants will also be considered)


Please apply to the Computer Science PhD program
(or potentially to the Statistics PhD program if a Statistics PhD is of direct interest).

For additional information see:

Center for Machine Learning and Intelligent Systems:
http://cml.ics.uci. edu/faculty/

UCI Computer Science Department:

UCI Data Science Initiative:

NSF NRT PhD Fellowships:

Students are also encouraged to visit individual faculty Web pages for more detail on individual research groups and to directly contact faculty that they have a specific interest in working with.