Post-doctoral Research Fellow (Transforming Collections)


Main Location: CCW - Millbank, London UK
Hours: 37
Contract: Fixed Term
Salary: £39,275.00 - £47,120.00 per annum
Closing date: November 28, 2021 11:55 pm

The role

We are seeking a Post-doctoral Research Fellow (PDRF) with experience designing and building Machine Learning (ML) systems for end users to work on the 3-year research project funded by the AHRC's Towards a National Collection programme, Transforming Collections: Reimagining Art, Nation and Heritage

As PDRF, you will report to Co-Investigators based at UAL’s Creative Computing Institute (CCI), working towards the design and development of Machine Learning (ML) systems for end-users through interaction with partners from 15 collections. You will collaborate with Co-Investigators across other strands of the project in order to synthesise outcomes and support the design and delivery of participatory workshops/seminars with Project Partners, Collaborating Organisations and diverse stakeholders, integrating findings from these workshops into the design of new tools. You will have responsibility for developing and maintaining ML software and interfaces using Git, and academic reporting (e.g. conference/symposium papers and journal articles).

You will participate in regular project team meetings and a peer support network of the project’s researchers, developing interdisciplinary research expertise and develop skills and experience in collaborative, ethical and participatory workshop design and delivery. You will present and/or publish case studies in progress through the local, national and international spaces and networks generated by the project.

Why choose us?

Transforming Collectionsis a £3m 3-year ‘Discovery Project’ funded as part of the major UKRI / AHRCprogramme,Towards a National Collection. Led by UAL’s Decolonising Arts Institute (DeAI) in collaboration with CCI, we will work closely with Tate as our Independent Research Organisation partner, and a further 14 Project Partners and Collaborating Organisations. Transforming Collections aims to enable cross-search of collections, surface patterns of bias, uncover hidden connections, and open up new interpretative frames and ‘potential histories’ (Azoulay, 2019) of art, nation and heritage. It will combine critical art historical and museological research with participatory ML design, and embed creative activations of interactive ML in the form of artists digital commissions.  

DeAI seeks to challenge colonial and imperial legacies and drive cultural, social and institutional change. We imagine the Institute as a decentred, disruptive, evolving and porous space, creating interdisciplinary collaborations and research-driven projects through partnerships across UAL and beyond. Working at the intersection of creativity and computational technologies, CCI explores the technology shaping our world and supports interdisciplinary teaching, research and knowledge exchange.

Your Profile

You have a PhD in a relevant area of arts, design, communication or related area completed within the past 5 years, with an excellent record of research dissemination (e.g. through exhibition, performance, publication and conference presentation).  

You will have the ability to lead the academic and/or professional development of your own area of expertise and to communicate ideas clearly and persuasively, summarising and interpreting complex, conceptual and specialist matters/information accurately.  

You will have experience of designing and building ML systems for real-world use by end users who are not ML experts, for instance in application domains such as the Arts, Digital Humanities, Healthcare, Accessibility, etc.  

You will have experience in building and training ML models for Natural Language Processing and/or Media (sound/image) analysis using Python, and experience integrating user centred design outcomes into the development and evaluation of new technologies. 

Applicants are asked to provide links to any software they have been involved in developing and/or to any published research they have in a supporting document as part of their application.

Should you have any queries please contact us on RKEE

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