Are you looking for a paid PHD in Data Science? You are inquisitive, you like to work independently but also as part of a team? You want to make an important scientific contribution? If so, we are looking forward to meeting you!
As part of the DDAI Comet module (explainable, verifiable and privacy-preserving data-driven AI) we offer a PHD position in the area Social Computing.
Personalized recommender systems are indispensable in today’s online world. Recommendation algorithms support users in finding resources (e.g., documents, movies, music) in large and complex information spaces such as e.g. the Web. A major drawback of many algorithms is that they lack transparency, which makes it hard for users to assess why an algorithm provides a specific recommendation.
We are looking for a PhD student, who is interested in research on designing and evaluating explainable recommender systems. Working at the intersection of recommender systems and human-computer-interaction, the candidate is expected to:
The dissertation work will be carried out in the Social Computing team of Elisabeth Lex and linked to existing research on recommender systems in this group.
The dissertation will be supervised at the Doctoral School of Computer Science at Graz University of Technology by Univ.-Prof. Dr. Stefanie Lindstaedt and Ass. Prof. Dr. Elisabeth Lex.
The minimum salary for this full-time position (38.5 h/w) is € 2,750 gross per month (14 times a year). There is a willingness to overpay depended on experience and qualifications.
Please submit your application with a motivational statement, a detailed CV and a current transcript of records at email@example.com.Meer informatie
|Titel||PHD: Explainable Recommender Systems (m/f)|
|Job location||Inffeldgasse 13/6 A-8010 Graz, Graz|
|Gepubliceerd||november 7, 2019|
|Vakgebieden||Informatica,   Informatiewetenschappen,   Algoritmen,   Interactie tussen mensen en computers,   Informatiesystemen (Bedrijfsinformatica),   Programmeertalen,   Computertechniek (Computer engineering),   Machinaal Leren  |