PhD Candidate (all genders) BIH QUEST Center (Team “Responsible Health Data Analysis”)

Share

Copy link

Work time

full-time

Start date

01.10.2026

Employment period

limited

Application deadline

17.08.2026

Deployment location Charité

BIH Berlin Mitte

Jobcode

7959

Salary group

E13

Working at Charité

The Berlin Institute of Health at Charité (BIH) is dedicated to biomedical translation. Its mission is to translate research findings into personalized prevention, diagnostics, and therapies to benefit patients and provide the scientific community with effective tools. With approximately 750 employees, the BIH specializes in translational method development, precision medicine, regenerative therapies, and biomedical data science. Closely integrated with Charité, the BIH promotes excellent research and facilitates the accelerated transfer of new discoveries into clinical practice through its supporting platforms and programs. Through these efforts, the BIH builds strong partnerships and fosters innovation-driven medicine in both national and international contexts.

The QUEST Center for Responsible Research at the Berlin Institute of Health (BIH) at Charité develops and implements new approaches to support the trustworthy conduct of biomedical research and the delivery of useful results in accordance with ethical standards.

The third-party-funded junior consortium project 3P-CAUSAL (“Promises, Pitfalls, and Pathways for Causal Inference with Synthetic and Anonymized Health Data”) will be carried out jointly by BIH, Charité – Universitätsmedizin Berlin, and the University of Trier. For the subproject “Causal Framework for Anonymized and Synthetic Health Data,” led by Dr. Jessica L. Rohmann, we are seeking a PhD candidate (all genders), starting on 01.10.2026 and ending on 30.09.2029 in full-time (38.5h/week).

The position focuses on developing a causally-grounded framework for evaluating anonymized and synthetic health data in the context of the privacy-utility trade-off. The role includes contributions to methodological development and evaluation, as well as application and validation using clinical and routine healthcare data (e.g., from the Medical Informatics Initiative (MII) and the Network of University Medicine (NUM)). The successful candidate with also contribute to cross-project tasks involving data access, governance, sharing, and consortium coordination.

The interviews are expected to take place during the week of August 24.

What you can expect

Develop and further refine a causally-grounded methodological framework for evaluating anonymized and synthetic health data in the context of the privacy-utility trade-off

Independently design new metrics to assess causal reliability (“causal utility”) and analyze the effects of data-modifying procedures on causal identification and estimation

Investigate and evaluate anonymization and synthetic data generation methods with regard to their suitability for causal research questions in biomedical research

Apply, implement, and validate the developed methods using clinical and routine healthcare data from infrastructures of the Medical Informatics Initiative (MII) and the Network of University Medicine (NUM)

Actively participate in a multi-site, interdisciplinary research consortium and independently contribute to and coordinate tasks relating to data access and governance

Actively contribute to scientific publications and to the development of tools and materials that support the application and use of the results in applied health research

Present