Hello,

we have 2 PhD positions on topics related to machine learning and privacy (NAUSICA: Privacy-aware transparent decisions group, Dept. Computing Science, Umeå, Sweden).

  Deadline: August 22nd.

  Conditions: 4 years contract (1st year, 28400 kr/month, about 2.700EUR).
  + the position includes research funding (e.g. for conference travel).

  To apply:
https://www.umu.se/en/work-with-us/open-positions/2-phd-student-positions-in-computer-science-with-focus-on-data-privacy_415620/

Our research group is interested in privacy-aware transparent AI systems. We want to understand the fundamental principles that permit us to build these systems, and develop algorithms for this purpose. We focus on data privacy for data processing, privacy-aware machine learning for building models and data analytics, and decision models for making decisions. AI systems need to cope with uncertainty in order to be deployed in the real world, where imprecision, vagueness and randomness are rarely absent. We research on approximate reasoning (models of reasoning to address uncertainty as e.g. probability-based, and fuzzy set-based models).

The project is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. https://wasp-sweden.org/

Umeå is a clean and green city in northeast Sweden on the Ume river and near the coast. It is a university town capital of the Vasterbotten county and the largest city in northern Sweden.

  For additional information: Vicenc Torra <vto...@cs.umu.se>
  My web page: http://mdai.cat/vtorra/

vicenc

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vicenc torra, Dr. Professor                   http://www.mdai.cat/vtorra
Dept. Computing Science, Umeå University, Sweden Editor: Transactions on Data Privacy http://www.tdp.cat
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Books:                     http://www.springer.com/gp/book/9783319573564
V. Torra, Data Privacy: Foundations, New Developments and
                                 the Big Data Challenge, Springer, 2017.
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