Zalando Doctoral Scholarship on Recommender Systems and Personalization
Zalando is a rapidly growing company, aiming at creating the best online fashion experience, and working on cutting edge personalization technologies and recommendations. This 36-month position will be at TU Darmstadt and will work closely with Zalando.
Zalando is known for rapid growth and innovative Marketing driven by a young and dynamic team; behind the scenes, it is aiming at creating the best online fashion experience. To that end Zalando is working on cutting edge personalization technologies and recommendations. The Knowledge Mining & Assessment Group at TU Darmstadt headed by Prof. Dr. Ulf Brefeld focuses on machine learning and information retrieval. The cooperation with Zalando offers an outstanding access to novel problem settings and real data at enterprise-level scales. We will collaborate closely with the Recommendations Group and the Data Intelligence Team at Zalando. Regular visits at the Zalando Headquarters in Berlin are encouraged.
Requirements for application
Excellently qualified graduates from the disciplines of computer science, mathematics, statistics, and related studies are invited to apply. Successful candidates are expected to possess very good programming skills in Python (Hadoop, Java, C/C++ skills are a bonus), to work independently, demonstrate their personal commitment, team and communication skills as well as a readiness to cooperate. Experience in recommender systems, machine learning, and information retrieval and processing methods would be appreciated.
The scholarship is granted for 36 months for completing a doctoral thesis. The successful candidate will be granted 1,400 Euros per month (tax free). Women and people with disabilities are expressly invited to submit their application.
The Department of Computer Science at TU Darmstadt regularly ranks among the top in Germany. Among its distinguishing features are its research initiative "Knowledge Discovery on the Web" focusing on powerful language technology procedures, text mining, machine learning and scalable infrastructures for assessing and aggregating knowledge.
Please submit your application by February 4, 2013. Applications should include a letter of motivation, CV and details regarding previous scientific work, certifications of studies and work, including the graduate thesis and possibly electronic publications.
Applications as well as inquiries should be sent to Stefanie Schmahl firstname.lastname@example.org .