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    <b><span style="font-size: 18.0pt;font-family:"Times New
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        lang="EN-GB">Research Fellow in Machine Learning and Deep Neural
        Architectures for Signal Analysis and Prediction <o:p></o:p></span></b>
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        School of Computer Science, University of Lincoln, UK<o:p></o:p></span></b><span
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        Salary:  </b> From £32,004 per annum</span><br>
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      post, for a fixed term of 27 months <br>
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        Date:  </b> Thursday 19 October 2017</span><br>
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        Date:  </b> Friday, 3 November 2017</span><span
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        Reference:  </b>COS417<o:p></o:p></span><span
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      We seek to employ a highly motivated post-doctoral researcher that
      will be working on the EU-H2020 project "CORe monitoring
      Techniques and EXperimental validation and demonstration
      (CORTEX)". The project will give the opportunity to the post
      holder to engage with other partners of the project across Europe.
      You should hold a PhD (or equivalent) or be near to completion,
      and should be able to demonstrate a good track record in at least
      one of the following research fields:</span><span
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      Machine Learning,</span><span
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      Deep Learning,</span><span
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      Signal Processing Applications,</span><span
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      Pattern Analysis.</span><span style="font-size:12.0pt;
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      <br>
      The contract is fixed for 27 months</span><span
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      lang="EN-GB">. The starting time can be soon following the
      announcement of the successful candidate.    </span><span
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      <br>
      This position is funded by the EU-H2020 project CORe monitoring
      Techniques and EXperimental validation and demonstration, which
      runs from September 2017 to August 2021. The CORTEX project aims
      at developing core monitoring techniques that can be used to
      detect and characterize operational problems, before they have any
      inadvertent effect on plant safety and availability. The project
      relies on using the fluctuations existing in any process parameter
      that can be measured at the plants in order to monitor their
      state, primarily the neutron flux. The postholder will work on
      applying machine learning and deep learning techniques for
      analysing the collected signals and produce effective and
      transparent predictions. <br>
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      You can apply on-line through the University of Lincoln web site.
      <o:p></o:p></span><br>
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      If you would like to know more about this opportunity, please
      contact <b style="mso-bidi-font-weight:normal">Professor Stefanos
        Kollias</b> (Founding Professor of Machine Learning, </span><span
      lang="EN-GB"><a href="mailto:skollias@lincoln.ac.uk"
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