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Research Associate in Machine Learning for Visual Data Analysis

Communications and Information Systems Group (CISG)

As part of our work within a number of EPSRC- and EU-funded projects, the UCL Electronic & Electrical Engineering, Communications and Information Systems Group invites applications for one (1) postdoctoral research position in Machine Learning for Visual Data Analysis.

For the last 50 years, the holy grail of machine learning with visual data has been to translate pixels to concepts, e.g., classify a pixel-domain video according to its contents (“tennis match”, “cooking show”, “person driving a van”,…), or find video scenes that are semantically similar to the contents of a given query video. However, pixel-domain video representations are in fact known to be cumbersome for machine learning, due to: limited frame rate, too much redundancy between successive frames, calibration problems under irregular camera motion, blurriness due to shutter adjustment under varying illumination, and very high power requirements. Inspired by biological vision, new input modalities are now beginning to be considered for visual data analysis, e.g., neuromorphic visual sensors, a.k.a., silicon retinas, or compressed-domain motion and RGB information from video codecs like MPEG/ITU-T AVC/H.264 and HEVC instead of uncompressed (pixel-domain) video.

 We are looking for a talented postdoctoral researcher to join our team and help us fulfil the projects’ goals, producing quality research in transfer learning or discriminative domain adaptation for visual data analysis and recognition problems, including, but not limited to, the problems and data modalities mentioned above. The work will involve design, development and implementation work and publishing high quality research papers in high-ranked conferences and journals. The successful candidates will work within an established research team in the Communications and Information Systems Group led by Dr Yiannis Andreopoulos.


  • (essential) related publications in computer-vision or image-processing oriented IEEE/ACM conferences and journals of high standing.
  • (essential) fluency in Python and Matlab programming, evidenced by previous usage in research papers
  • (essential) fluency in the use of machine learning libraries like Caffe, Tensorflow, Keras or similar, evidenced by use in data problems, competitions or research publications
  • (desired) strong skills in using the terminal environment in Linux or MacOS, and potentially exposure to front-end tools in HPC or cloud computing (e.g., Jupyter or other front ends for machine learning and data science)
  • (desired) knowledge of the state-of-the-art in image/video classification, and event or action recognition.


Algorithm design skills are essential and a strong publication record is also important. The work will involve the design of new models, algorithms and protocols, some of which will need to be implemented in the projects’ testbeds, an effort that will be led by the successful candidate.

The position is available from September 2018 for 24 months in the first instance. Further funding to support the post may be available.

The candidates are required to relocate to London.

Salary is dependent on qualifications and experience. The Research Associate post is Grade 7, with salary between £34,635 and £41,864 per annum, depending on prior experience, both including London Allowance which is currently £3,031 per annum.

If the successful candidate has not yet been awarded their PhD, appointment will be made as a Research Assistant (Grade 6). Payment at Grade 7 will be backdated to the date of final submission of the PhD thesis including corrections, once the PhD has been awarded.

Research Assistant: Grade 6B, point 24-26. Salary range £30,316 to £31,967 (inc. London Allowance of £3,031 pa).

Interested applicants are encouraged to make informal enquiries about the posts to Dr Yiannis Andreopoulos (

Applications should be submitted via the UCL Online Recruitment system.

Job Reference: 1732352

You can also download a copy of the advert and job description.

If you have any queries regarding the application process please contact Vicky Coombes ( quoting reference 1732352

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