Stellenticket Freie Universität Berlin

The Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin (Prof. Sebastian Schelter, DEEM Lab, https://deem.berlin & Prof. Matthias Böhm, DAMS Lab, https://www.tu.berlin/dams ) is looking for a research associate to work on an “agility project” in data engineering and recommender systems. The research project will be conducted in close collaboration with the “Information Retrieval Lab” of Prof. Maarten de Rijke at the University of Amsterdam in the Netherlands, https://irlab.science.uva.nl .

The research project revolves around enforcing the “right-to-be-forgotten” in recommender systems to empower users to efficiently remove their personal data from such systems. This is especially important in recommender systems that assist people in critical use cases such as finding medical supplies, food or care products for their children. Unfortunately, existing recommender systems are designed to maximise prediction quality and lack “unlearning” and data removal functionality, which can lead to devastating consequences: Imagine a person struggling with alcohol addiction, who decides to stop consuming alcoholic products. Unfortunately, this person will still be exposed to recommendations for alcohol products online, since the underlying ML models will have learned their preference for alcohol.

On a technical level, the research project will focus on the following questions: How can we benchmark unlearning methods in recommender systems with respect to unlearning guarantees and response latency? How can we augment existing state-of-the-art recommendation algorithms with unlearning functionality? How can we efficiently execute the unlearning operations at scale?

The goal of the research project is to develop the algorithmic foundations and their corresponding efficient execution strategies for sub-second unlearning in recommender systems. Furthermore, we aim to implement an open source recommendation library with unlearning functionality to contribute to the public infrastructure for responsible data management. Teaching tasks.

• Successfully completed university degree (Master, Diplom or equivalent) in Computer Science or Artificial Intelligence
• Strong programming skills in Python and at least one additional language (Java/Scala/Rust/C++)
• Knowledge of machine learning and recommender systems
• Experience working with relational databases and/or data processing systems such as Apache Spark, DuckDB, etc.
• The ability to teach in German and/or in English is required; willingness to acquire the respective missing language skills.

Not required, but helpful:

• Practical experience with real-world ML applications and MLOps
• Experience with regulations such as GDPR and the EU AI Act
• Contributions to open source projects
• Creative and independent mindset, self-motivated work style

Please send your written application, quoting the reference number, with the usual application documents to Technische Universität Berlin - Die Präsidentin - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, Management of Data Science Processes (DEEM Lab) Group, Prof. Dr.-Ing. Schelter, EN-728, Einsteinufer 17, 10587 Berlin or by e-mail (one PDF Datei, max. 5 MB) tu: schelter@tu-berlin.de.

For cost reasons, application documents sent by post will not be returned. Please submit copies only.

By submitting your application via email you consent to having your data electronically processed and saved. Please note that we do not provide a guaranty for the protection of your personal data when submitted as unprotected file. Please find our data protection notice acc. DSGVO (General Data Protection Regulation) at the TU staff department homepage: https://www.abt2-t.tu-berlin.de/menue/themen_a_z/datenschutzerklaerung/ .

To ensure equal opportunities between women and men, applications by women with the required qualifications are explicitly desired. Qualified individuals with disabilities will be favored. The TU Berlin values the diversity of its members and is committed to the goals of equal opportunities.