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CfACTs Postdoctoral Research Fellow - Machine Learning and Artificial Intelligence for Computer Generated Images

Bournemouth University

Location: Bournemouth
Salary: £50,317.00 per annum (pro rata)
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 23rd November 2021
Closes: 27th March 2022
Job Ref: FMC257

(Fixed-Term, 3 Positions Available)

Salary £50,317.00 per annum (pro rata)

Key words: Machine Learning, Artificial Intelligence, GAN, Rendering, Hair Modelling, Facial Modelling

Bournemouth University’s vision is worldwide recognition as a leading university for inspiring learning, advancing knowledge and enriching society through the fusion of education, research and practice. Our highly skilled and creative workforce is comprised of individuals drawn from a broad cross section of the globe, who reflect a variety of backgrounds, talents, perspectives and experiences that help to build our global learning community.

The jobs are available to all nationalities. The postdoctoral research fellow will join the Centre for Applied Creative Technologies (CfACTs), a Marie Skłodowska-Curie Actions (MSCA) COFUND research training centre that provides international mobility and cross-sectoral experience through a well-aligned research and training programme.

This is an excellent opportunity for those with research and/or development experience in machine learning and artificial intelligence (such as using GAN, implementing image processing solutions, developing computer vision algorithms), with the prospect of further career development.   

The fellow will be an employee at Bournemouth University, and embark on a two-year programme with one of the industrial partners:

  • Framestore, a globally known VFX-company, who has a proud history creating extraordinary images and scenes for some of Hollywood’s biggest pictures;
  • Humain, who create realistic digital humans for world leading AAA video game developers such as Microsoft and Activision (Call of Duty)

To be eligible to apply an applicant must meet the requirements outlined and:

  • Be in possession of a doctorate or have at least four years full-time equivalent research experience.
  • Not have resided or carried out his/her main activity (work, studies, etc.) in the United Kingdom for more than 12 months in the three years immediately before the call deadline.

Please REGISTER YOUR INTEREST here to receive dedicated support during application.

https://forms.office.com/r/nyGC5pJTpq

Please see the BU‑CfACTs webpage for guidance for applicants: www.bournemouth.ac.uk/cfacts-research

Available Research Themes to Choose (details at www.bournemouth.ac.uk/cfacts-research ):

Description

Required Knowledge/Skills

Theme 1 (Framestore): apply machine learning for

photorealistic rendering

Skills: machine learning; GAN; rendering algorithms.

Experience: computer vision, computer generated graphics, or photorealistic rendering (e.g. fur/hair).

Theme 2(Framestore): learned resolution for

photorealistic rendering

Skills: machine learning; GAN; image upsampling.

Experience: computer vision, photorealistic rendering, or image processing.

Theme 3(Humain): automatic facial modelling/rigging

Skills: related deep learning algorithms;

Experience: facial recognition, facial landmark tracking, or image/video processing; 

Applicants must submit the following for independent evaluation:

  • Research Proposal (max.10 pages)
  • CV (max.5 pages)

The documents must use templates available at: www.bournemouth.ac.uk/cfacts-research

This position is available on a fixed-term basis for 24 months.

For additional information please contact Prof Jian Chang, via  CfACTs-enquiries@bournemouth.ac.uk.

A detailed job description and person specification are available from our website together with an online application form.  Alternatively please email the HR Services Team at hrvacancies@bournemouth.ac.uk quoting the appropriate reference (FMC257)

BU values and is committed to an inclusive working environment.  We seek a diverse community through attracting, developing and retaining staff from different backgrounds to contribute to inspirational learning, advancing knowledge and enriching society.  To support and enable our staff to achieve a balance between work and their personal lives, we will also consider proposals for flexible working or job share arrangements.

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