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Two funded PhD positions available

Closing date: noon (GMT), January 12th 2024
See below for full details

PhD on "The Striatum as a Recurrent Neural Network"

A fully-funded project available in Nottingham's BBSRC Doctoral Training Program (DTP)
Application deadline: noon, January 12th 2024
Apply here


The massive, silent striatum controls our behaviour. When it falters, movement disorders ranging from Parkinson’s disease to the tics of Tourette’s result. Keeping its two output pathways in balance seems key to maintaining our ability to control our behaviour. The canonical model for the striatum predicts that these dual output pathways compete to respectively select or suppress behaviours represented by cortical inputs. But recent advances in cell-specific imaging and optogenetics have brought strongly dissenting data: both pathways are similarly co-active during behaviour, and stimulating either pathway both lacks the predicted opposing effects on downstream neurons and does not have the predicted effects on behaviour. A new model of the striatum is thus essential.

In this project, we will test the hypothesis that the striatum is a special class of recurrent neural networks (RNNs) that use purely inhibitory connections. We will build and analyse this class of networks, deriving predictions for the computations that striatum performs, and for the  activity of neuron populations in the striatum. We will then test these predictions in two large-scale datasets of population recordings from striatum in freely-exploring mice from the studies of Klaus et al (Neuron, 2017) and Markowitz et al (Cell, 2018).


PhD on "Optimising patient selection for Deep Brain Stimulation in Parkinson’s disease using multimodal machine learning"

A fully-funded 4-year studentship 

Application deadline: noon, 12th January 2024

Apply here

Parkinson’s disease has debilitating motor symptoms of tremor in the limbs, slowness of movement, and freezing, unable to move. A highly effective treatment is electrical stimulation deep in the motor regions of the midbrain. But surgery for this deep brain stimulation is only offered to around 2% of all patients, and about a quarter of those who receive it have poor outcomes. Optimising the selection of patients for deep brain stimulation will widen access to treatment, improve treatment outcomes, and prevent harm. 

The goal of this project is to test how fusing clinical data, neuroimaging, and video assessments could optimise the selection of patients. The project will be in collaboration with MachineMedicine (London), a MedTech company specialising in Parkinson’s disease, and the movement disorders clinical team at St George’s Hospital, London. In joining this collaboration, the PhD student will be trained in data-science and machine learning tools, including how to extract and analyse MRI and fMRI data, in fusing data across modalities, and in developing a machine-learning pipeline for predicting patient outcomes. These predictions will be tested against the 12-month follow-up data from the St George’s trial patients. The student’s further training will include a 3-month placement at MachineMedicine, and visits to St George’s clinic.

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