Gatsby Computational Neuroscience Unit

Entry requirements

Applicants must have a strong analytical background, a keen interest in neuroscience or machine learning and a relevant first degree at a minimum of upper second-class UK Bachelor's level or an overseas equivalent, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics. Students seeking to combine work in neuroscience and machine learning are particularly encouraged to apply.

Months of entry

September

Course content

Students at the Gatsby Unit study toward a PhD in Computational and Theoretical Neuroscience and Machine Learning. You will network extensively with the Sainsbury Wellcome Centre and other UCL experimental groups. Gatsby is part of the Centre for Computational Statistics and Machine Learning, together with UCL Computer Science and Statistical Science. Approximately 90% of alumni have secured academic or industry positions.

The Gatsby Unit is a world-class centre for Computational and Theoretical Neuroscience and Machine Learning. Our research seeks to understand the principles of learning, perception and action in brains and machines by developing mathematical algorithms. We provide a unique opportunity for a critical mass of theoreticians to interact closely and network extensively with each other and with other research groups across UCL, in particular the Sainsbury Wellcome Centre, and the Centre for Computational Statistics and Machine Learning. First year teaching is supplemented with weekly research talks, journal clubs and reading groups, external seminar programmes and participation in international conferences. As a student you will be supported by all members of academic staff, not just your immediate supervisors.

Department specialisms

-Analysis of neural data -Neural dynamics -Neural plasticity -Perceptual processing of auditory and visual input -Neural population coding -Kernel methods -Bayesian statistics -Reinforcement learning -Statistical machine learning -Unsupervised learning -Network and relational data