Decision, Optimization, and Learning at Caltech (DOLCIT)
Decision, Optimization, and Learning at Caltech (DOLCIT) supports postdoctoral scholars in multidisciplinary computing and mathematical sciences research that connects with other science and engineering disciplines.
Funder: California Institute of Technology
Due Dates: January 1, 2027 (Full consideration) | Open until filled (Shared CMS listing)
Funding Amounts: Postdoctoral term appointment
Summary: DOLCIT supports postdoctoral research within Caltech’s multidisciplinary Computing + Mathematical Sciences Department.
Key Information: Appointments are contingent on completion of the PhD.
Description
Decision, Optimization, and Learning at Caltech (DOLCIT) is a postdoctoral fellowship program funded and hosted by the California Institute of Technology through its Computing + Mathematical Sciences (CMS) Department. Included in CMS’s current postdoctoral recruitment, it supports research in a multidisciplinary environment spanning computer science, applied mathematics, communication and networks, and control and systems.
DOLCIT forms part of an interdisciplinary postdoctoral community that typically includes more than 20 scholars. Across CMS, postdoctoral positions are sponsored by individual faculty, research groups, and cross-cutting research centers. The department’s research connects computing and mathematical sciences with other scientific and engineering disciplines. Scholars may also choose to teach a course, with departmental permission, as part of their postdoctoral training.
