DOLCIT Postdoc (Decision, Optimization, and Learning at the California Institute of Technology)
The Decision, Optimization, and Learning at the California Institute of Technology (DOLCIT) research group announces postdoctoral openings starting Fall 2017. Areas of interest are: decision theory, machine learning, optimization, statistics, and data-driven methods broadly construed.
DOLCIT brings together people from machine learning, optimization, applied math, statistics, control, robotics, and human-computer interaction to form an intellectual core pertaining fundamental and applied research in "Decision, Optimization, and Learning at the California Institute of Technology." DOLCIT envisions a world where intelligent systems seamlessly integrate learning and planning, as well as automatically balance computational and statistical tradeoffs in the underlying optimization problems.
A CV, a Research Statement, and three reference letters (up to a maximum of five) are required. A cover letter and a Teaching Statement are optional. Applications will be reviewed starting January 1, 2017.
Caltech is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability status, protected veteran status, or any other characteristic protected by law.
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