Gatsby Computational Neuroscience Unit, UCL 4 year PhD Programme

Gatsby Computational Neuroscience Unit, UCL 4 year PhD Programme

Posted by Rebecca Martin on Sat, 18/10/2008 - 16:22

The Gatsby Unit is a centre for theoretical neuroscience and machine learning, focusing on unsupervised, semi-supervised and reinforcement learning, neural dynamics, population coding, Bayesian and nonparametric statistics and applications of these to the analysis of perceptual processing, neural data, natural language processing, machine vision and bioinformatics. It provides a unique opportunity for a critical mass of theoreticians to interact closely with each other, and with other world-class research groups in related departments at UCL (University College London), including Anatomy, Computer Science, Functional Imaging, Physics, Physiology, Psychology, Neurology, Ophthalmology and Statistics, with the cross-faculty Centre for Computational Statistics and Machine Learning, and also with other UK and overseas universities notably, at the present time, with Cambridge in the UK and Columbia, New York.

The Unit always has openings for exceptional PhD candidates. Applicants should have a strong analytical background, a keen interest in machine learning and/or neuroscience and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics.

The PhD programme lasts four years, including a first year of intensive instruction in techniques and research in theoretical neuroscience and machine learning.

Competitive fully-funded studentships are available each year (to students of any nationality) and the Unit also welcomes students with pre-secured funding or with other scholarship/studentship applications in progress.

Full details of our programme, and how to apply, are available at: http://www.gatsby.ucl.ac.uk/teaching/phd/

For further details of research interests please see: http://www.gatsby.ucl.ac.uk/research.html

Applications for 2009 entry (commencing late September 2009) should be received no later than 11 January 2009. Shortlisted applicants will be invited to attend interview in the week commencing 9 March 2009.