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PhD Industrial CASE studentship - Deep Learning for Face Image Analytics - Development of strategies for extracting facial attribute knowledge from deep learning architectures on image data

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  • Full or part time
    Dr J Bacardit
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Number of awards:


Start date and duration:

Academic year 2018/2019 for 4 years.

Application closing date:

We would like to keep this advert open until a suitable candidate has been identified.


We invite applications for a PhD studentship with title "Development of strategies for extracting facial attribute knowledge from deep learning architectures on image data, and their links to age and health"

The overall aim of this project is to explore the capacity of deep machine learning techniques to analyse and extract age relevant information from facial image data. Deep Learning techniques excel at processing complex data, and synthesising high-level features capturing valuable knowledge. Together with the recent creation of high-quality facial image databases, deep learning is now positioned to enable new analytic strategies for identifying features that predict age and other health-related characteristics from facial images and replicating (or surpassing) human abilities.

In this studentship project you will face the challenge of developing innovative strategies to leverage the power of deep learning algorithms (arguably the fastest growing Artificial Intelligence paradigm) and extract clinically-relevant health and well-being knowledge in academic and industrial research environments.

Applicants will need to show experience in (a combination of) the following skills:
•Strong machine learning background and proficiency in the state of the art data science languages (e.g. R, python)
•Proficient programming skills
•Deep Learning
•Knowledge discovery
•Information visualisation
•Experience in real-life applications
•High Performance Computing (e.g. classic HPC clusters, GPUs, Intel PHI, Big Data frameworks, Cloud resources)

The studentship will include an internship period at Unilever R and D (co-sponsor).


Jointly funded by the Engineering and Physical Sciences Research Council (under the ’Industrial CASE’ scheme) and Unilever R and D.

Name of supervisor(s):

Dr Jaume Bacardit (, School of Computing (, and Professor Michael Catt (, Director, National Innovation Centre for Ageing (

Eligibility Criteria:

Applicants should have a first class degree, or a combination of qualifications and/or experience equivalent to that level. Ideally, students should have a BSc or MSc degree in computer science.

Applicants should be strong programmers, and experience in machine learning will be greatly valued. The eligibility of the award follows EPSRC rules.

How to apply:

You must apply through the University’s online postgraduate application system. To do this please ‘Create a new account’ (

All relevant fields should be completed, but fields marked with a red asterisk on the online admissions portal must to be completed. The following information will help us to process your application. You will need to:
•insert programme code 8050F in the programme of study section
•select ‘PhD Computer Science (full time)’ as the programme of study
•insert the studentship code COMP008 in the studentship/partnership reference field
•attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code COMP008 and state how your interests and experience relate to the project
•attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.

Please send your covering letter and CV by e-mail to [Email Address Removed].

Funding Notes

100% of UK tuition fees paid and an annual stipend of £14,777, plus an enhanced annual stipend of £2,000 contributed by Unilever R and D.

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