If you are interested in joining our group as a visiting researcher at any level (Bachelor, Master, PhD, Postdoc) we would love to hear from you. While we typically do not offer any funding, we offer close supervision, great office space, and an awesome learning experience. If you like, you can work on real-life systems like our recommender-system-as-a-service Mr. DLib or Darwin & Goliath. In that case, your work will help thousands of users to get better recommendations. Or, you do a ‘normal’ R&D project with the latest state-of-the-art machine-learning and recommender-system technologies and algorithms.
We had dozens of visitors already and the vast majority was highly successful. By ‘successful’ we mean that the students learned new skills and technologies, could apply their existing skills, and finish their visit with a publication at a conference or workshop. To give you some examples, the following publications all resulted from visiting Bachelor & Master students in 2019.
Philipp Scharpf, Ian Mackerracher, Moritz Schubotz, Joeran Beel, Corinna Breitinger, and Bela Gipp. “AnnoMathTeX – a Formula Annotation Recommender System for STEM Documents.” In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys), 2019.
Beel, Joeran, and Victor Brunel. “Data Pruning in Recommender Systems Research: Best-Practice or Malpractice?” In 13th ACM Conference on Recommender Systems (RecSys), 2019. HTML Version
Edenhofer, Gordian, Andrew Collins, Akiko Aizawa, and Joeran Beel. “Augmenting the DonorsChoose.org Corpus for Meta-Learning.” In Proceedings of The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR), 32–38. CEUR-WS, 2019.
A research visit in our group will prepare you for a research or industry career in machine learning and recommender systems. Our previous students nowadays work for Google, Amazon, or Facebook, run their own Startups, or pursue PhDs at prestigious universities (e.g. UC Berkeley).