PhD Candidate: Search and Recommendation Systems that Learn from their Users 253 views

The Institute for Computing and Information Sciences (iCIS) at Radboud University is looking for a PhD candidate to study how search and recommendation systems can learn from complex user behaviour and optimise goals that go beyond simple user-engagement.

About the position:
  • Candidates excited to turn original and creative ideas into theory and algorithms that have a real impact on the search and recommendation field are encouraged to apply.
  • The selected candidate will pursue PhD in the data science group at iCIS, a vibrant international research environment with top-class professors and researchers in information retrieval, causality and machine learning.
  • The selected candidate build upon the counterfactual approach to learning from user interactions where user behaviour is first modelled and the resulting model is used to correct for bias during optimisation.
  • This is a unique area that integrates aspects from the field of information retrieval as well as from that of machine learning, where work will be on methods that have a strong theoretical grounding but also are widely applicable to real-world search and recommendation services.

Requirements:

  • Applicant must hold Master’s degree in Computer Science, Artificial Intelligence or a similar discipline.
  • Good programming skills in Python or similar computer languages.
  • Affinity with machine learning and statistics.
  • A good command of spoken and written English.

Applications:

Apply and the application should include the following attachments:

  • Letter of motivation.
  • A link to the PDF of a publication the applicant is most proud of, or deems relevant to the position (if applicable).
—————–Quick Overview————-
Organization Radboud University
Country Netherlands
Fellowship Level Doctoral
Subject areas Computer Science, Artificial Intelligence
Fellowship amount Varies
Eligibility Open to all nationalities
Deadline 30 November 2020
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