The School of Computer Science and Electronic Engineering at the University of Essex is pleased to announce a PhD studentship available in “Bayesian Deep Learning for Alzheimer's conversion prediction in Mild Cognitive Impairment subjects”.
This three-year studentship will start from 23 April 2018 and includes: # A fee waiver equal to the Home/EU fee (for 2017/18, £4,120). International students will need to pay the balance of their fees. # A stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£14,553 in 2017-18) Mild cognitive impairment (MCI) is a transitional state between normal ageing and dementia. In a number of cases, MCI carries the risk of conversion to Alzheimer?s disease-related dementia. MCI typically includes slowing of motor performance and information processing, impaired attention and impaired executive functions with partial preservation of memory. Machine learning techniques have recently been identified as promising tools in neuroimaging data analysis and can, to a certain extent, work on a single patient basis in predicting conversion from MCI to Alzheimer?s disease (AD). This PhD will investigate novel convolutional and Bayesian deep learning techniques to identify biomarkers of MCI and improve the accuracy of detecting early signs of the potential for MCI to progress into AD. Early AD diagnosis is important for giving access to treatments that can improve symptoms and slow down the progress of the disease. The successful applicant will be supervised by Dr Luca Citi and Dr Alba García and will be part of the Essex BCI and Neural Engineering Lab (http://essexbcis.uk): today the UK's largest research group in brain-computer interfaces. *Deadline: Friday 23 February 2018* http://www.jobs.ac.uk/job/BHF171/phd-studentship-bayesian-deep-learning-for-alzheimers-conversion-prediction-in-mild-cognitive-impairment-subjects/
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