PhD Position in Responsible Data Engineering (salary level E13)
We are looking for a PhD student to conduct research in responsible data engineering. The research will focus on data preparation and data pipelines for complex machine learning (ML) systems. Such ML systems are increasingly used to automate impactful decisions but suffer from many unsolved data management challenges with respect to their correctness, reliability, and compliance with legal regulations.
The goal of the research will be to design and efficiently implement data-centric methods to make ML systems guarantee their users control over their personal data (e.g., with respect to the "right-to-be-forgotten" from GDPR) and adhere to legal regulations such as the upcoming European AI Act.
This will be achieved via novel declarative methods to create, maintain and assess datasets for ML use cases. These will assist non-expert users with data-centric tasks, such as evaluating the robustness of their ML pipelines to data errors and potentially leverage the code generation capabilities of large language models. The resulting methods will be accompanied by efficient and scalable implementations and made publicly available as open source libraries. Teaching tasks.
Requirements
- 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/Rust/C++)
- Knowledge in data processing with dataflow systems, relational databases and/or dataframe libraries (e.g., Apache Spark, DuckDB, pandas, etc.)
- Experience with increasing the efficiency, scalability and correctness of data-centric programs
- Basic knowledge of machine learning and common libraries (e.g., pandas, sklearn, pytorch, SparkML, etc.)
Not required, but nice to have
- First-hand experience with real world data processing systems and/or ML deployments (e.g., from internships, jobs or entrepreneurial experience)
- Contributions to open source projects
How to apply: Please send your application with the usual documents by e-mail to Prof. Dr. Sebastian Schelter at schelter [at] tu-berlin [dot] de , quoting the reference number IV-22/25, until 6th of June 2025.
PhD Position in Responsible Data Engineering (salary level E13)
We are looking for a PhD student to conduct research in responsible data engineering. The research will be conducted in close collaboration with Prof. Julia Stoyanovich from New York University. Responsible data engineering is emerging as a new discipline at the intersection of data engineering and AI that treats ethics, legal compliance, and inclusivity as central design considerations. The holistic nature of this approach is based on the observation that the decisions we make during data collection and preparation profoundly impact AI systems we build and deploy.
The goal of this position is to create a new system which helps data engineers to design data preparation pipelines that optimize model performance along a rich set of responsibility objectives, including accuracy, robustness, fairness, and legal compliance. For that, the system will proactively guide data engineers through the selection and evaluation of a large set of data preprocessing, data augmentation and feature selection operations. A reliable, efficient and easy-to-use open source implementation of this system will be created as part of the research project.
This endeavor is technically challenging in multiple ways. First, data preparation and model selection need to be optimized for multiple objectives, in contrast to existing approaches, which focus on a single objective only such as overall prediction accuracy. Second, the system will have to create, rewrite and concurrently execute large numbers of different pipeline variants, which requires an efficient runtime and novel query optimization techniques. Third, the research needs to account for current dramatic changes in the development practices of AI applications, e.g., AI assisted programming, tabular foundation models and AI-based data science agents.
Requirements
- Successfully completed university degree (Master, Diplom or equivalent) in Computer Science or Artificial Intelligence
- Very good programming skills in Python and at least one additional language (Java/Rust/C++)
- Knowledge in data processing with dataflow systems, relational databases and/or dataframe libraries (e.g., Apache Spark, DuckDB, pandas, etc.)
- Experience with increasing the efficiency, scalability and correctness of data-centric programs
- Basic knowledge of machine learning and common libraries (e.g., pandas, sklearn, pytorch, SparkML, etc.)
Not required, but nice to have
- First-hand experience with real world data processing systems and/or ML deployments (e.g., from internships, jobs or entrepreneurial experience)
- Contributions to open source projects
How to apply: Please send your application with the usual documents by e-mail to Prof. Dr. Sebastian Schelter at schelter [at] tu-berlin [dot] de , quoting the reference number IV-177/25, until 20th of June 2025.
Student employee (40h/month)
We are currently looking for two student employees to support our research in tasks such as the implementation of research prototypes (for data validation, machine unlearning, feature stores, recommender systems), the collection, generation and preparation of training data or the analysis of program code with large language models. The students will also be listed as co-authors on the resulting scientific publications.
Requirements
- Very good knowledge of scalable data processing or machine learning
- Very good knowledge of programming with Python or Java/C++/Rust
Please consult the official job ad for details on how to apply.
Prof. Dr. Sebastian Schelter
Dr. Arnab Phani
Celia Bohnhardt-Schneider
Hao Chen
Olga Ovcharenko
Pierre Lubitzsch
Zeyu Zhang
Shubha Guha
Till Doehmen
Yichun Wang
Stefan Grafberger
Aynaz Abdollahzadeh
Leonardo Dominici