Professorship in Data Engineering in Health (Charité, BIFOLD, ID: 679/2026)
Position Profile
At Charité – Universitätsmedizin Berlin, in cooperation with BIFOLD, the following position is to be filled at the earliest possible date at the Institute of Medical Informatics, CharitéCentre 1 (CC01) for Prevention, Human and Health Sciences: Professorship in Data Engineering in Health.
Salary Grade W3 BBesG ÜfBE – Permanent Tenure (Reference Number: Prof. 679/2026)
The appointment will be made as a professor under an employment contract pursuant to § 102 paragraph 5 of the Berlin Higher Education Act (BerlHG). The teaching load is governed by the Teaching Load Regulation for Berlin Universities (LVVO) and is reduced to 4 teaching hours per week (LVS).
Profile
Charité – Universitätsmedizin Berlin is one of the largest university hospitals in Europe, where physicians and scientists conduct research, provide patient care, and teach at an internationally leading level. Charité is the joint medical faculty of Freie Universität Berlin and Humboldt-Universität zu Berlin and is recognized worldwide as an outstanding institution for education and training. Charité operates across four campuses with approximately 100 clinics and institutes organized into 17 CharitéCentres. Charité holds certificates for the audit berufundfamilie® (work and family) and audit familiengerechte Hochschule® (family-friendly university).
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) was established in 2019 through the merger of two national AI competence centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the dynamic metropolitan region of Berlin, BIFOLD offers an excellent scientific environment and numerous collaboration opportunities for national and international researchers. It conducts foundational research in big data management and machine learning, as well as their intersections, with the aim of training future talent and fostering high-impact knowledge exchange.
Details
The professorship is intended to bring research on Big Data and Machine Learning into the applied context of clinical data management. Both foundational research in data management and multimodal machine learning, as well as their application in clinical environments, are to be further developed. Candidates with demonstrated expertise in health data engineering, data infrastructure, and FAIR data principles are particularly sought. Research performance must be evidenced by relevant publications and other scientific achievements appropriate to the career stage.
Appointment Requirements: In accordance with § 100 BerlHG, candidates must hold a successfully completed university degree in computer science or a comparable field, as well as a completed doctorate. Additionally, a professorship or junior professorship, a habilitation, or comparable scientific qualifications are required.
In addition to the above appointment requirements, the professorship is associated with the following expectations:
- Solid specialist knowledge and demonstrated scientific expertise in the methodological and systematic collection, integration, processing, analysis, and provision of biomedical data
- Experience applying modern data science methods – such as deep learning, multimodal learning, and self-supervised learning – to complex and heterogeneous clinical data
- An original (independent) research portfolio in the field of data engineering, demonstrated through relevant publications and successful acquisition of third-party funded projects
- Outstanding international reputation, e.g., membership in committees of national and international professional societies
- Extensive teaching experience and excellent didactic skills
Applicants must demonstrate through their previous scientific work that they meet the stated requirements for the W3 professorship, that they are prepared to actively shape scientific activities in the field of Data Engineering in Health, and that their research complements existing activities at Charité in these areas.
Starting Date: As soon as possible
Application Deadline: April 10, 2026
Detailed Job Description (only in German language): Job ID Prof. 679/2026