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Research Positions in Machine Learning at Paderborn University

Description

The Intelligent Systems Group at Paderborn University is seeking for highly qualified doctoral or postdoctoral researchers interested in machine learning. Candidates are expected to conduct research within a project funded by the German Research Foundation. The contract is for three years, and the payment is determined according to the competitive German TVL E-13 scheme (depending on the candidate's experience and qualifications). Within the project, there is a possibility for a cooperation with Robert Busa-Fekete from Yahoo! Research, New York.
P O S I T I O N R E Q U I R E M E N T S
Ph.D. position applicants need to combine excellent skills in mathematics, statistics, and computer science. A successful postdoc applicant should have a strong background in machine learning with a corresponding track record of research publications, including top-tier conferences (e.g., ICML, NIPS, AISTATS, IJCAI, AAAI) and journals (e.g., JMLR, MLJ). Ideally, an applicant has experience on topics relevant for the project (see below).
H O W T O A P P L Y
Ph.D. applicants should provide a research statement, their CV, degrees including grade-sheets, and two references who are willing to write a recommendation letter. Postdoc applicants should additionally provide their top three publications. Please submit complete applications, preferably combined in a single PDF file, to Prof. Eyke Hüllermeier (eyke-AT-upb.de). Please state the reference number 2847 in the subject. There is no fixed deadline, but the positions will be filled as soon as possible.
T H E P R O J E C T
In machine learning, the notion of multi-armed bandit (MAB) refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. Combining theoretical challenge with practical usefulness, MABs have received considerable attention in machine learning research in the recent past. This project is devoted to a variant of standard MABs that is referred to as the dueling bandit or preference-based multi-armed bandit (PB-MAP) problem. Instead of learning from stochastic feedback in the form of real-valued rewards for the choice of single alternatives, a PB-MAB agent is allowed to compare pairs of alternatives in a qualitative manner. The goal of this project is to address several open research questions related to the PB-MAB setting, and to study variants and extensions of this setting.
M O R E I N F O R M A T I O N
The homepage of the Intelligent Systems group can be found here:
https://www.cs.uni-paderborn.de/fachgebiete/intell...

Last modified: 2017-08-19 13:45:47