**** Department of Computer Science, University of Copenhagen **** 

The Machine Learning Section of the Department of Computer Science at the Faculty of Science at the University of Copenhagen (DIKU) is offering five fully-funded PhD Fellowships in Machine Learning and Information Retrieval, commencing 1 January 2022 or as soon as possible thereafter.

- Deadline to apply: August 15, 2021

- Link to apply: https://employment.ku.dk/phd/?show=154480

* Our group and research, and what do we offer:

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The fellows will join the Machine Learning Section at DIKU. The Machine Learning section is among the leading research environments in Artificial Intelligence and Web & Information Retrieval in Europe (in the top 5 for 2020, according to csrankings.org), with a strong presence at top-tier conferences, continuous collaboration in international & national research networks, and solid synergies with big tech, small tech, and industry. The Machine Learning section consists of a vibrant selection of approximately 65 talented researchers (40 of whom are PhD and postdoctoral fellows) from around the world with a diverse set of backgrounds and a common incessant scientific curiosity and openness to innovation.

* The fellows will conduct research, having as starting point the following broad research areas:

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- a fully-funded PhD in machine learning evaluation;

- a fully-funded PhD in bias and interpretability for machine learning;

- a fully-funded PhD in overparameterization and generalizability in deep neural architectures;

- a fully-funded PhD in applied machine learning and/or information retrieval with focus on human-centered computing aspects;

- a fully-funded PhD in web & information retrieval.

* Who are we looking for?

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We are looking for candidates with a MSc degree in a subject relevant for the research area. The successful candidate is expected to have strong grades in Machine Learning and/or Information Retrieval. For one of the PhDs, the candidate is expected to also have strong grades in Human-Centered Computing. The candidate should have a preliminary research record as witnessed by a master thesis or publications in the area.

For more information, please have a look at: https://employment.ku.dk/phd/?show=154480