PhD Position in Industrial Artificial Intelligence: Customisable Private Deep Learning

In collaboration with City, University of London, the School of Mathematics, Computer Science & Engineering, and Delta Capita Ltd, EIT Digital Doctoral School is offering a PhD studentship. The studentship falls under the new EIT Digital Industrial Artificial Intelligence Doctoral Programme at City, University of London. The studentship consists of a full fee waiver and a stipend of £18K per year, for four years. As part of their studies and training, students will spend time at City, University of London, EIT Digital London Co-Location Centre and Delta Capita Ltd premises. In addition, over the 4 years of study, the PhD will spend between 3 and 6 months abroad to enrich their research experience, for which a supplementary budget is available. EIT Digital also provides a European training in innovation, entrepreneurship and digital transformation leadership at other EIT Digital centres across the EU.

The PhD project, entitled “Customisable Private Deep Learning”, is focused on the design and evaluation of end to end, secure, efficient privacy-preserving deep learning. Although current literature includes several proposals to run deep learning with privacy-preservation, the performance is still far from satisfactory. This PhD project will focus on using current technology (homomorphic encryption/secure multi-party computation/differential privacy/federated learning) in a principled approach to design customised solutions for use cases, that improve the computational efficiency and performance of deep learning architectures under given privacy constraints.

The appointed candidate will have a degree and a MSc in Computer Science or an area related to Artificial Intelligence and cloud computing. Given the nature of the project, experience in industry is advisable.

Applications, consisting of a CV and a Personal Statement, should be submitted to EIT Doctoral School Office at Closing date: 17:00 BST, 15 September 2019. 

Interviews are scheduled for the week commencing the 23 September 2019.

The role is available from 15 October 2019 or earlier by negotiation.

For further information about the position please contact us at

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