Internship: Bayesian Inference and Learning
The Control and Dynamical Systems (CD) group at MERL is seeking a highly motivated intern to advance the frontiers in the intersection of bayesian inference and learning. The ideal candidate has previous experience with at least some of sequential Monte Carlo methods, Gaussian processes, Kalman filtering, and target tracking. The candidate is expected to possess strong abilities in algorithm development and analysis. The internship start date is flexible. The duration is approximately 3 months, with possible extension. Publication of the results produced during the internship is expected.
Contact: Jay Thornton
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