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Academic/Research positions in Business Analytics, Data Science, Machine Learning in May 2016


Academic/Research positions Analytics and Data Science in Los Angeles-CA, Cardiff-Wales, and Oslo-Norway.



Here is a list of Academic/Research positions in Business Analytics, Data Mining, Data Science, Machine Learning posted on KDnuggets in May 2016.

See an up-to-date and full list at www.kdnuggets.com/academic/

See also industry jobs in business analytics, Big Data, Data Mining, and Data Science.

Academic Positions 2016 Feb

  • Data Scientist at USC, Los Angeles, CA. - May 31, 2016.
    Looking for a data scientist to join our team and create analytical solutions for business problems in fundraising. This position is responsible for generating solutions for existing problems and studying the business to ask (and answer) questions nobody has asked before.
  • Research Assistant (Rugby Union) Data Scientist / Machine Learning Specialist at Cardiff Metropolitan University, Cardiff, Wales. - May 19, 2016.
    A full-time Data Science position is available to support a collaborative research project between Cardiff Metropolitan University and World Rugby. The project aims to; examine the subsequent injury risk following concussion in professional Rugby Union players, and to develop an associated software analysis tool using machine learning.
  • PhD Position in Intelligent Recommender Systems at Oslo and Akershus University, Oslo, Norway. - May 10, 2016.
    The area of research is recommender systems and information retrieval systems. The project will investigate the enhancement of Intelligent Recommender Systems with novel artificial intelligent tools such as Deep Learning. The data available for this project consists of book-records with both professional meta-data and user-generated content.
  • PhD Position in AI, Machine Learning at Oslo and Akershus University, Oslo, Norway. - May 10, 2016.
    AI-based Software Tool for Memory Training for Dementia: We want to develop different methods within statistics/machine learning (personalized learning) to optimize test/learning tools procedures. In particular, input from the patients while they use the test/learning tool will be used to gradually optimize the tools.

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