The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.
This innovative course has been developed in close partnership between Oxford University and IBM Research. Each research project has been co-developed by Oxford academics working with IBM scientists. Students will have a named IBM supervisor/s and many opportunities for collaboration with IBM throughout the studentship.
The scientific focus of the programme is at the interface between Physical and Life Sciences. By bringing together advances in data and computing science with large complex sets of experimental data more realistic and predictive computational models can be developed. These new tools and methodologies for computational discovery can drive advances in our understanding of fundamental cellular biology and drug discovery. Projects will span the emerging fields of Advanced Molecular Simulations, Machine Learning and Quantum Computing addressing both fundamental questions in each of these fields as well as at their interfaces.
Students will benefit from the interdisciplinary nature of the course cohort as well as the close interactions with IBM Scientists.
Applicants who are offered places will be provided with a funding package that will include fees at the Home rate, a stipend at the standard Research Council rate (currently £17,668 pa) + £2,400 for four years.
There are 16 projects available and you may identify up to three projects to be considered for in your application. The details of Project 8 are listed below.
There is no application fee to apply to this course. For information on how to apply and entry requirements, please see https://www.ox.ac.uk/admissions/graduate/courses/dphil-computational-discovery
Title: Optimising therapy for brain disorders through AI-refined deep brain stimulation
PI: Hayrie Cagnan
Brain stimulation is extensively used to modulate neural activity in order to alter behaviour. In recent years, closed-loop stimulation techniques have gained increasing traction to sense a biomarker such as elevated neural activity patterns, and deliver stimulation in time with such events. Closed-loop stimulation techniques are used both for establishing a causal link between behaviour and neural activity, and also to treat various neurological and psychiatric conditions. Building on our recent work (West et al 2022, Cagnan et al 2017), this PhD project aims to formalise stimulation parametrisation by using theoretical models of brain circuits in combination with state of the art machine learning approaches. Specifically, we will train artificial neural networks to classify discrete brain states of interest and optimise stimulation parameters to achieve precise manipulation of activity propagating across brain circuits. The successful development of such an approach would provide a powerful framework to guide next generation stimulation strategies both for usage in basic science and clinical applications.