PhD Studentship in New Methods for One-shot Learning using Bayesian Deep Learning
University of Cambridge - Department of Engineering
|Funding for:||UK Students, EU Students|
|Funding amount:||Not specified|
|Placed on:||2nd November 2016|
|Closes:||4th January 2017|
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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.
The position will involve research with Dr. Richard E. Turner developing new methods for one-shot learning using Bayesian deep learning. One-shot learning involves making dramatic inductive leaps from just a single datapoint. The project will involve developing state-of-the-art technology based upon Bayesian neural networks, Gaussian Processes and new approximate inference techniques.
The Machine Learning Group is internationally renowned, comprising about 30 researchers, including Dr. Turner, Prof. Zoubin Ghahramani, Prof. Carl Rasmussen, and Dr. Miguel Hernandez Lobato.
For further information contact Dr Richard Turner - firstname.lastname@example.org
Applicants should hold (or be expected to hold) a degree in Information Engineering, Electrical Engineering, Statistics, Physics, or Computer Science preferably with 1st class honours (or equivalent). Some practical experience of machine learning or statistics would be strongly preferred (e.g. course work assignments or research).
This 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 of EPSRC Web site. Overseas students are not eligible and should not apply.
Applications should be made on-line via the Cambridge Graduate Admissions Office before the deadline: www.admin.cam.ac.uk/students/gradadmissions/prospec/apply, with Dr Richard E. Turner identified as the potential supervisor.
The University values diversity and is committed to equality of opportunity.
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South East England