[Dear colleagues, i would be grateful if you can disseminate this advert
through your channels]
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* 1 4-year PhD Industrial CASE studentship in Deep Learning for Face
* Image Analytics
* Location: School of Computing, Newcastle University, UK
* Start date: Academic year 2018/2019
* Application closes when a suitable candidate is identified.
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* Value of award
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&D.
* Description
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&D
(co-sponsor).
* 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 (https://epsrc.ukri.org/skills/students/help/eligibility/).
* How to apply
You must apply through http://www.ncl.ac.uk/postgraduate/apply/
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
[log in to unmask]
Applications will close once a suitable candidate is identified, so we
would like to encourage to apply early.
* Contact
For informal enquiries, please email [log in to unmask]
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Jaume Bacardit, PhD
Reader in Machine Learning
Newcastle University
The Interdisciplinary Computing and Complex BioSystems research group.
Web: http://www.ico2s.org/
Twitter: @ico2s
School of Computing, Newcastle University.
1 Science Square, Science Central, Newcastle upon Tyne, NE4 5TG, UK
Email: jaume _dot_ bacardit _at_ newcastle.ac.uk _dot_ ac _dot_ uk
Web: http://homepages.cs.ncl.ac.uk/jaume.bacardit
Twitter: @jaumebp
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