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Offer 2 out of 226 from 29/07/26, 14:49

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Berlin Institute of Health at Charité (BIH) - BIH QUEST Center

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.

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

English

Tasks:

  • 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 the results at scientific conferences

Requirements:

  • A successfully completed academic university degree (Master’s or Diplom) in data science, medical informatics, biostatistics, bioinformatics, mathematics, epidemiology, health data science, or a related data analysis or computer science discipline
  • A strong interest in health data analysis research, digital medicine, and the application of modern methods to answer biomedical research questions
  • The ability to independently familiarize yourself with new methodological questions and address them in a structured manner
  • Strong programming skills in R and/or Python for preparing, analyzing, and visualizing complex biomedical datasets
  • Proficiency in reproducible analysis workflows, including version control, documentation, and transparent research practices
  • Very good written and spoken English; German language skills are not required but would be advantageous for engagement with national infrastructures and stakeholders
  • Strong teamwork and communication skills, as well as an independent, structured, and careful working style, a strong sense of responsibility, and initiative
  • Experience working with complex or high-dimensional health data is desirable, as is an interest in causal inference methods and a willingness to become familiar with the relevant research approaches quickly

What we offer:

  • A varied job in a forward-looking research institute
  • Pay group E13 TVöD VKA-K. The classification is based on qualifications, the respective experience level is calculated on the basis of professional experience. The annual salary (gross) is stated for a full-time position without special or additional payments.
  • Additional benefits customary in the public sector (including annual special payment, company pension scheme (VBL), capital-forming benefits)
  • Flexible working hours and the option of working remote
  • 30 vacation days per year (with a five-day week)
  • Various support offers to balance work and family life (childcare, cooperation with voiio)
  • Training and further education opportunities
  • Mobile citizens' office on site
  • Corporate benefits (travel, leisure, shopping, etc.), Wellhub, JobRad
  • Very easily accessible and attractive workplace at the Spreepalais am Dom, Anna-Louisa-Karsch-Str. 2, 10178 Berlin

For more information about the job posting, please visit our website.