Postdoctoral Research Position in Recommender Systems TU Delft, Netherlands (10 Nov deadline)

Delft University of Technology (TU Delft) is currently recruiting a post-doctoral researcher for a period of two years, to work within the CrowdRec project (crowdrec.eu), devoted to the development and evaluation of next-generation recommender systems that fuse multiple information sources.

The researcher will work closely with the Multimedia Computing Group team, and will:

- Develop algorithms in the area of recommender systems, including context-aware, socially-informed, and crowd-driven approaches.
- Publish in top venues, e.g., ACM RecSys, ACM Multimedia.
- Contribute to the CrowdRec Reference Framework, which publishes reference implementations of developed algorithms.
- Work together with CrowdRec partners around Europe, including Barcelona, Milan, Budapest, to carry out large-scale testing of recommender algorithms.
- Guide students at all levels (Bachelor, Master, and PhD) who are carrying out work in the area of recommender systems and multimedia.
- Contribute to the innovation of new research challenges and the development/organization of community benchmarks.
- Participate in the reporting and analysis of research results, including project meetings, and roadmapping meetings with the CrowdRec External Advisory Board.
- Develop new research areas and project concepts in collaboration with colleagues working on other projects in the Multimedia Computing Group (e.g., in the areas of music, social networks, quality of multimedia experience).

*Requirements*
The successful candidate will have:
- A PhD in a field related to multimedia and/or recommender systems.
- A proven track record of research leading to publications in top venues.
- A background in machine learning.
- Programming skills necessary to participate in the implementation of reference implementations (Java, C++, C# or Python).
- Experience with the organization of international research events, especially research challenges or benchmarks.
- Interest in working closely with companies to carry out large-scale evaluation of recommender systems.
- Interest in or experience with the application of crowdsourcing or gamification techniques in multimedia.

*Conditions of employment*
TU Delft offers an attractive benefits package, including a flexible work week and the option of assembling a customized compensation and benefits package (the 'IKA'). Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. Contract type: Temporary, 2 years

For more information about this position, please contact Martha Larson, phone: +31 (0)15-2787357, e-mail: m.a.larson@tudelft.nl. To apply, please e-mail a detailed CV, publications list and two references along with a letter of application by 10 November 2014 to Mrs. C.J.C.Kohlmann van Noord, Hr-eemcs@tudelft.nl.
When applying for this position, please refer to vacancy number EWI2014-27.

*Delft University of Technology*
Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognized level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.

*Electrical Engineering, Mathematics and Computer Science (EEMCS)*
The Department of Intelligent Systems (INSY) within the EEMCS faculty conducts research on processing and interpretation of data to address the increasing volume and complexity of data and communication in today's world.

The Multimedia Computing Group within the INSY Department develops algorithms for enriching, accessing, and searching large quantities of data. The focus is on developing systems that are oriented to the needs of users, making possible satisfying, personalized interaction with large collections of multimedia data (e.g., images, video, and music shared online) in real-world contexts (e.g., within social networks). The group combines expertise on multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, and quality of experience.

Additional information
Martha Larson
+31 (0)15-2787357
m.a.larson@tudelft.nl