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Position: Research Assistant in Machine Learning for Visual Data Analysis
Institution: University College London
Department: Electronic & Electrical Engineering
Location: London, United Kingdom
Duties: 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
Requirements: Applicants are required to have a Masters degree (or 4 or 5-year undergraduate degree) in Computer Science, Electronic Engineering or a related field. Fluency in Python and Matlab programming evidenced by previous usage in research papers is essential, as is an 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. It is desirable that applicants have 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. The full person specification can be found in the job description
   
Text: Research Assistant in Machine Learning for Visual Data Analysis, - Ref:1732339 Click here to go back to search results Apply Now UCL Department / Division Electronic & Electrical Engineering Location of position London Grade 6 Hours Full Time Salary (inclusive of London allowance) ?30,316 - ?31,967 per annum Duties and Responsibilities As part of our work within a number of EPSRC and EU-funded projects, the UCL Electronic & Electrical Engineering , Communications and Informations Systems Group invites applications for a 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. The position is available from September 2018 for 24 months in the first instance. Key Requirements Applicants are required to have a Masters degree (or 4 or 5-year undergraduate degree) in Computer Science, Electronic Engineering or a related field. Fluency in Python and Matlab programming evidenced by previous usage in research papers is essential, as is an 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. It is desirable that applicants have 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. The full person specification can be found in the job description. Further Details Applications can be made by clicking on the 'Apply Now' button below. A copy of the job description can also be downloaded below. Interested applicants are encouraged to make informal enquiries about the post to Dr Yiannis Andreopoulos (i.andreopoulos@ucl.ac.uk). Any questions regarding the application process should be directed to Vicky Coombes (v.coombes@ucl.ac.uk) UCL Taking Action for Equality Closing Date 26 Jul 2018 Latest time for the submission of applications 23:59 Interview date TBC Our department is working towards an Athena SWAN award. We are committed to advancing gender equality within our department. This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff. Please use these links to find out more about the UCL Terms and Conditions related to this job, employee benefits that we offer and further information about UCL . Job Description Apply Now
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