PhD Industrial CASE studentship in Deep Learning for Face Image Analytics

Postgraduate Opportunities

Newcastle University

Reference code: COMP008

Closing date: We would like to keep this advert open until a suitable candidate has been identified

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

Sponsor: Jointly funded by the Engineering and Physical Sciences Research Council (under the 'Industrial CASE' scheme) and Unilever R and D

Duration of the award: 4 years

Person Specification

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.


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).

Study information

Start month:

Academic year 2018/2019

Funding information

Funding applies to:
Other: see Funding notes
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. The eligibility of the award follows EPSRC rules

Contacts and how to apply

Academic contact:

For further details, please email Dr Jaume Bacardit,

Administrative contact and 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 email to