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Research Assistant 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) junior research assistant 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. At the same time, exciting developments in transfer learning and discriminative domain adaptation allow for knowledge transfer from one data modality to another, thereby opening new opportunities to advance the state-of-the-art in resource-efficient visual data analysis that can be deployed in practical systems.

We are looking for a talented research assistant 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 candidate will work within an established research team in the Communications and Information Systems Group led by Dr Yiannis Andreopoulos.

Applicants are required to have a Masters degree (or 4 or 5-year undergraduate degree) in Computer Science, Electronic Engineering or a related field. They also need to have knowledge and/or hands-on experience in one or more of the following areas:

 

  • (essential) understanding of data science and machine learning, evidenced by high marks in related graduate-level modules or completion of related online courses in Coursera or similar.
  • (essential) fluency in Python and Matlab programming, evidenced by previous usage in research papers
  • (desired) some exposure in the use of machine learning libraries like Caffe, Tensorflow, Keras or similar, evidenced by extensive use in data problems, competitions or research publications
  • (desired) experience 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).

 

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 assisted by other members of the team and the successful candidate is expected to contribute to.

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

Possibility to register as part-time PhD student in the Department may be made available.

The candidates are required to relocate to London.

Salary is dependent on qualifications and experience. The Research Assistant post is Grade 6B, with salary between £30,316 and £31,967 per annum, depending on prior experience, both including London Allowance which is currently £3,031 per annum.

Interested applicants are encouraged to make informal enquiries about the post to Dr Yiannis Andreopoulos (i.andreopoulos@ucl.ac.uk).

Applications should be submitted via the UCL Online Recruitment System.

Job reference: 1732339

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 (v.coombes@ucl.ac.uk) quoting reference 1732339

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