Ph.D. vacancy in Machine learning for Novel Imaging Systems, co-funded by Amazon and QuantIC

Ph.D. vacancy in Machine learning for Novel Imaging Systems at the University of Glasgow, supervised by Prof. Roderick Murray-Smith

Available immediately, part-funded and run in collaboration with Amazon Research and QuantIC , the UK Quantum Technology Hub in Quantum Enhanced Imaging, .

There will be opportunities for applying for internships at Amazon’s labs in Cambridge, Berlin or Seattle during the Ph.D.

The project will use machine learning frameworks such as Deep Convolutional networks and autoencoders with novel imaging techniques, including single-pixel cameras, hyperspectral imaging, 3D from ultra-precise timing and low-photon count imaging. The work will use novel imaging systems developed within the QuantIC hub.

The stipend funding is at standard UK rates (£14,553), and includes fees. Please note: this stipend and fees are only available for UK/EU citizens.

Please apply by sending a c.v. to ASAP to Roderick.Murray-Smith@glasgow.ac.uk with the final deadline being the 10th December 2017. The successful candidate will be able to start immediately.

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Bea Vad presents at ISMIR 2015 in Spain

Bea at ISMIR

Bea presented the paper:
B. Vad, Boland, D., Williamson, J., Murray-Smith, R., and Steffensen, P. B., Design and evaluation of a probabilistic music projection interface, In: 16th International Society for Music Information Retrieval Conference, Malaga, Spain, 26-30 Oct 2015.

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A conceptual model of the future of input devices

Speaker: John Williamson
Date: 14 October, 2015
Time: 14:00 – 15:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Turning sensor engineering into advances into human computer interaction is slow, ad hoc and unsystematic. I’ll discuss a fundamental approach to input device engineering, and illustrate how machine learning could have the exponentially-accelerating impact in HCI that it has had in other fields.

 

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Seminar: Engaging with Music Retrieval

Engaging with Music Retrieval

Speaker: Daniel Boland
Date: 09 September, 2015
Time: 14:00 – 15:00
Location: Sir Alwyn Williams Building, 422 Seminar Room

Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Using listening histories and work from music psychology, a set of engagement-stratified profiles of listening behaviour are developed. The challenge of designing music retrieval for different levels of user engagement is explored with a system allowing users to denote their level of engagement and thus the specificity of their music queries. The resulting interaction has since been adopted as a component in a commercial music system.

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