You are here: Home / Vacancies / PhD Studentships / PhD Studentship: Signal and Image Processing for Art Investigation

PhD Studentship: Signal and Image Processing for Art Investigation

A fully-funded three-years PhD studentship is available to Home UK / EU students to work on ‘Signal and Image Processing for Art Investigation’ within the context of a recently funded EPSRC project “ARTICT | Art Through the ICT Lens”.

PhD Studentship: Signal and Image Processing for Art Investigation

Duration of study: Full Time- three years fixed term

Starting date: 1st October 2018

Application deadline: 31st July 2018 (or until filled)

Supervisor: Dr Miguel Rodrigues

A fully-funded three-years PhD studentship is available to Home UK / EU students to work on ‘Signal and Image Processing for Art Investigation’ within the context of a recently funded EPSRC project “ARTICT | Art Through the ICT Lens”. [1] The student will work under the supervision of Dr. Miguel Rodrigues within the Department of Electronic and Electrical Engineering, University College London.

Collaboration opportunities are also envisioned with the project partners, such as the National Gallery, London. The PhD student will also have the opportunity to intern with the group of project partner Professor Ingrid Daubechies, Duke University, in order to develop the research work further.

The cultural heritage sector is experiencing a digital revolution driven by the increasing availability of cutting-edge analytical and imaging techniques generating large multidimensional datasets. These techniques include (a) macro X-Ray Fluorescence (MA-XRF) scanning (b) Hyper-Spectral Imaging (HSI) and (c) traditional (digital) imaging such as X-Ray Radiography (XRR) and Infrared Imaging (IRR). See Figure 1.

This wealth of digital data (e.g. HSI datasets contain up to 600 Gb/m2) has the potential to support the technical study, conservation, preservation, or presentation of artwork within cultural heritage institutions. For example, it is suggested that the availability of complementary datasets, such as MA-XRF and HSI, can support the discovery, characterization and visualization of features of interest within the stratigraphy of paintings, including (1) preparatory sketches, (2) pentimenti, (3) concealed earlier designs, (4) later overpaint and retouchings, or, importantly, (5) the use of particular materials and pigments in different paint passages. However, the inability of traditional (primarily manual) approaches to adequately interrogate such large datasets calls for new algorithms to make sense of cultural heritage data.

 

The PhD student will develop new signal processing, image processing, and machine learning algorithms to address relevant data processing tasks arising in art investigation such as the visualization of the various painting layers associated with a painting (e.g. the concealed male figure in Fig. 1).

Francisco de Goya

Applicants must hold, or be near completion of a first or upper-second class degree in Engineering, Computer Science, or a related subject. The ideal candidate will show understanding of signal processing, image processing, machine learning, and computer programming. The candidate must also show a strong interest to engage in innovative high-profile research. Fluent English is also required.

 

Also, the candidate is expected to:

  • • Have excellent analytical and engineering skills
  • • Have excellent reporting and communication skills
  • • Be self-motivated, independent and team player
  • • Have genuine enthusiasm for the subject and technology
  • • Have the willingness to author and publish research findings in international high-profile journals
  • • Be eligible for home studentship:

 

https://www.epsrc.ac.uk/skills/students/help/eligibility/

 

The studentship is available for three years and covers tuition fees at the UK rate, plus a stipend at £16,777 pa (tax free).

Informal enquiries should be addressed to Dr Miguel Rodrigues (m.rodrigues@ucl.ac.uk) by 31st July 2018 (or until filled).

Formal applications should be submitted with a CV, a brief statement of your research interests, and with names and email addresses of two referees.