I am interested in supervising students with a strong mathematical, computational or neuroscience background. Projects could be carried out in several possible areas relating to the work in the group. Some suggestions for topics that would be interesting to me are below, but I'm very happy to consider other possibilities. In addition to working within the group, studying at Imperial College provides excellent opportunities for interacting with other theoretical and experimental researchers, both at Imperial (recently ranked 8th in the world in the QS world university rankings) and in the many neuroscience groups in London.
Applicants for a PhD position should initially send me a brief CV and cover letter with a description of research interests or a proposed project, and will eventually have to formally apply through the standard Imperial College mechanism (for more information, see here). There are several opportunities for funding which we can discuss if you are offered a position. Note that a masters degree is required for PhD study at Imperial: please see the PhD requirements page (and the Country-specific requirements).
I am also interested in supervising PhD students in neuroinformatics as part of the HiPEDS CDT (Centre for Doctoral Training in High Performance Embedded and Distributed Systems). Note that a masters degree is also required for entry.
I do not currently have any open postdoctoral positions, but please get in touch if you are interested in applying for your own funding through a fellowship scheme, for example.
- Theory of spiking neurons. How does the brain compute using spikes? Does this represent a new paradigm of computation that is substantially different from existing notions of digital and analogue computation? If so, what can it do better and how? Specific questions might look at the role of single cells versus networks, neural adaptation, and excitation/inhibition.
- Auditory neuroscience: I'm particularly interested in how the brain processes realistic auditory environments, where sound comes in a continuous stream, interesting signals can be both expected or unexpected, there can be multiple sound sources, background noise, etc. This could involve computational modelling or technological applications.
- Spiking neural network simulation. For example, running simulations on non-standard computational hardware, such as field-programmable gate arrays (FPGAs), graphics processing units (GPUs) and the SpiNNaker spiking neural network supercomputer. As neuroscientists study ever larger systems the need for large scale simulations is rapidly growing, but the techniques and hardware to do so are out of reach of the majority of neuroscience researchers. These innovative types of computational hardware have the potential to solve these problems, but substantial work is still required to implement realistic models on them in a way that is accessible to neuroscientists without deep technical training.
- Computational and analytical methods for the analysis of the neural "big data" that will be revolutionising neuroscience over the next decade. New hardware is becoming available which provides several orders of magnitude more data than were previously available, but there is not yet agreement on methods for using this data to understand how the brain functions.
- Real-time applications of spiking neural network models, for example in robotics. Until fairly recently, spiking neuron models were too computationally expensive to run on robotics platforms which require real-time operations. New developments have made this possible, but there are still very few studies which do so, despite the huge promise of these types of models.
- Games for research. For example, using computer games to retrain our sensory processing, or to generate data as part of a "citizen science" or "crowd sourcing" initiative.