Research Data Manager (m/f/d) Clinical AI Infrastructure
Universitätsklinikum Carl Gustav Carus Dresden sucht: Research Data Manager (m/f/d) Clinical AI Infrastructure in Dresden. Die Stelle ist als Teilzeit, Vollzeit ausgeschrieben. veröffentlicht am 07.09.2026.
- Arbeitgeber
- Universitätsklinikum Carl Gustav Carus Dresden
- Ort
- Dresden
- Beschäftigungsart
- Teilzeit, Vollzeit
- Veröffentlicht
- 07.09.2026
Aufgaben und Anforderungen: Research Data Manager (m/f/d) Clinical AI Infrastructure bei Universitätsklinikum Carl Gustav Carus Dresden
Aufgaben
The position is embedded in an interdisciplinary and international research environment within the Else Kröner Fresenius Center for Digital Health at TU Dresden. While the institute has a strong focus on AI, this position has a broader clinical AI infrastructure mandate and will support projects across the center and its clinical partners that require access to routinely generated clinical data, prospective model evaluation, and robust research software and data infrastructure. The position is embedded into groups working at the intersection of AI development, clinical care, and translational research, with the goal of developing and clinically evaluating AI tools that improve patient care. These groups currently lead several externally funded projects involving large-scale surgical video data, electronic health record data, and multicenter clinical datasets.
As Data Manager/Research Data and Software Engineer, you will play a central and cross-cutting role across clinical AI projects. This is a hands-on data, software, and infrastructure role. Your work will help establish secure access to live or near-real-time clinical data, enable prospective shadow-mode evaluations (“shadow trials”) of AI models without influencing clinical decision-making, and build and maintain reusable research infrastructure. You will ensure that multimodal data along patients' treatment pathways are reliably linked, securely stored and managed, and made available to researchers in a well-organized, privacy-compliant, and reproducible manner.
- Design, implement, and maintain data pipelines that integrate multimodal, structured and unstructured clinical and surgical data from proprietary data collection systems and electronic health record systems, supporting both retrospective analyses and secure live or near-real-time data flows in coordination with the local data integration center at TU Dresden
- Develop and maintain interfaces with clinical data systems using established healthcare interoperability standards, in particular FHIR, and support reliable event-driven or scheduled data exchange for clinical AI applications
- Establish and operate workflows for prospective shadow-mode evaluations (“shadow trials”), including automated model execution on live or near-real-time data, model and data versioning, logging, performance monitoring, and audit-ready capture of results without affecting clinical care
- Implement and maintain automated and semi-automated workflows for data de-identification and anonymization of clinical patient data, including video and image data, in compliance with applicable data protection regulations (GDPR and hospital data privacy requirements)
- Oversee and further develop the institute's data infrastructure, including databases and storage systems, organization, access control, backup procedures, long-term archiving, and documentation of clinical, imaging, video, and model-output datasets according to FAIR principles
- Build, maintain, and document reusable research software and computational infrastructure for clinical AI, including secure servers, containerized environments, databases, APIs, workflow orchestration, user access management, and coordination with TU Dresden's high-performance computing resources
- Implement and maintain practical data security and cybersecurity measures for the institute's research infrastructure; advise the group on data privacy, secure software operation, and security best practices
- Develop and maintain technical documentation, data dictionaries, provenance records, standard operating procedures, and reproducible onboarding materials for shared clinical AI infrastructure
- Liaise with the hospital's IT department, data protection officer, data integration center, clinical departments, and other infrastructure partners on data access, governance, prospective evaluation, and operational integration
- Support researchers across the group and collaborating clinical AI projects with data access, data quality questions, shadow-trial setup, and onboarding to shared infrastructure and workflows
Profil
- University degree (Bachelor's/Master's level) in medical informatics, computer science, bioinformatics, data engineering, or a related field
- Practical experience with FHIR and/or electronic health record systems (e.g., clinical data integration, HL7 interfaces, data integration centers, or hospital IT environments); experience with live, near-real-time, or event-driven clinical data flows is an advantage
- Experience with data de-identification or anonymization, ideally in a clinical or research context; familiarity with relevant tools and methods for video or image data de-identification is a plus
- Solid understanding of data privacy regulations and practical data security, including GDPR and IT security fundamentals; experience with access control, encryption, and secure data handling in research environments
- Experience setting up and maintaining data storage systems, databases, servers, APIs, containerized environments, or research computing infrastructure
- Proficiency in Python and/or other scripting languages for data pipeline development, automation, and research software engineering
- Experience with software engineering, DevOps, or MLOps practices such as Git, automated testing, CI/CD, Docker, workflow orchestration, deployment monitoring, or related tools is desirable
- Experience supporting prospective evaluations, silent deployment, shadow-mode testing, or operational monitoring of clinical algorithms is a plus
- Structured, reliable, and detail-oriented working style with a strong sense of responsibility for data quality and security
- Excellent communication skills and comfort working closely with both technical and non-technical colleagues, including clinicians and researchers
- Excellent English communication skills, willingness to learn/knowledge of German is a plus
Wir bieten
at the Else Kröner Fresenius Center for Digital Health
The position is available from October 1, 2026, full- or part-time, initially limited to 24 months, with the option of extension and longer-term collaboration. Compensation is based on the applicable collective bargaining agreement and, subject to fulfillment of the personal requirements, may be classified according to pay grade E13.
- Salary: According to the TV-L collective agreement, 65% of pay grade E13 (subject to meeting the personal requirements), plus an annual special payment.
- Annual Leave: 30 days of paid vacation.
- Professional Development: Extensive internal and external training opportunities to continuously develop your professional skills.
- Working Environment: A culture built on transparency, teamwork, and shared success. Join us for social events and sporting activities such as the REWE Team Challenge.
- Mobility: Financial support for public transportation through a subsidized job ticket.
- Health & Well-being: Access to modern fitness and exercise facilities, mental health programs, and a comprehensive occupational health management program.
- Employee Benefits: Discounts at our hospital pharmacy, exclusive corporate benefits, and additional shopping discounts.
- Work-Life Balance: Our Family Office provides support with childcare, holiday programs, and caregiving responsibilities. We also offer flexible working arrangements.
Herkunft dieser Stellenanzeige
Diese Stellenanzeige wurde von stepstone übernommen und erscheint auf ERP-Jobs.org. Die ursprüngliche Ausschreibung steht unter https://www.stepstone.de/stellenangebote----20260907---14477323-inline.html.