PhD studentship in Probabilistic methods in time series analysis and control

University of Cambridge - Department of Engineering

We are seeking a highly creative and motivated PhD student to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK.

Probabilistic methods in time series analysis and control To improve approximate Bayesian inference in non-linear dynamical systems, allowing for learning and control in the face of data scarcity, noise and uncertainty. Simultaneous solution to dynamics learning and control through experimental design.

The Machine Learning Group is internationally renowned, comprising about 30 researchers, including Dr. Turner, Prof. Zoubin Ghahramani, Prof. Carl Edward Rasmussen, and Dr. Miguel Hernandez Lobato.

For further information contact Professor Carl Rasmussen

Applicants should hold (or be expected to hold) a degree in Information Engineering, Electrical Engineering, Statistics, Physics, or Computer Science preferably with 1stclass honours (or equivalent). Some practical experience of machine learning or statistics would be strongly preferred (e.g. course work assignments or research).

This EPSRC funded studentship is available for Home and EU students. Home students and certain EU students will receive a full studentship including fees and Maintenance. EU students will receive a fees only award. Details on eligibility can be found On the EPSRC Website: overseas students are not eligible and should not apply.

Applications should be made on-line via the Cambridge Graduate Admissions Office before the deadline: , with Prof Carl Edward Rasmussen) identified as the potential supervisor.

The University values diversity and is committed to equality of opportunity.

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South East England