Introduction
The position is part of the research consortium Deciphering Metastasis with Multimodal Artificial Intelligence Foundation Models (DECIPHER-M) . The consortium is funded by the German Federal Ministry of Research, Technology and Space (BMFTR) and brings together clinical and technical expertise in medical AI, image analysis, genomics, big data, implementation research, and trustworthy AI across University Hospitals Dresden, Aachen, Essen, Munich, and Mainz, the German Cancer Research Center, and Helmholtz AI.
The advertised project focuses on the development, validation, and clinical integration of a modular Prevention Agent. The aim is to create an AI-supported system that enables personalized recommendations for cancer prevention and cancer screening across different clinical application scenarios. The focus of the advertised position lies on the technical integration of clinical data, their further processing, and their integration with local LLM and agent-based systems. Particular emphasis will be placed on on-premise deployments, traceable data processing, interoperability, and scalability within European healthcare systems.
Your responsibilities:
- Development and implementation of interfaces between clinical data sources, medical knowledge bases, and local LLM/agent-based systems
- Development of high-performance data pipelines for loading, structuring, validating, and further processing interoperable health data
- Contribution to the technical architecture of LLM agents
- Integration of structured and unstructured clinical information into agentic workflows for personalized cancer prevention and screening recommendations
- Development of APIs, data models, and software components for secure communication between clinical systems, AI modules, and user interfaces
- Design and implementation of local on-premise deployments of LLM-based components, taking into account data protection, IT security, traceability, and clinical usability
- Integration and further development of knowledge bases, retrieval-augmented generation components, and guideline modules for medically traceable recommendations
- Development of user interfaces and demonstrators for clinical users, study workflows, shadow evaluations, and feedback iterations
- Systematic evaluation of the developed interfaces and AI components with regard to technical robustness, efficiency, medical plausibility, provenance, guideline adherence, and applicability in clinical practice
- Close collaboration with clinical and technical project partners to translate clinical requirements into robust software solutions
- Documentation of the developed software, interfaces, and data flows, as well as contribution to scientific publications, conference contributions, and project reports
Your profil:
- Completed university degree in computer science, artificial intelligence, engineering, data science, or a related field
- Very good programming skills in Python, as well as experience in developing, structuring, and maintaining more complex software projects
- Sound knowledge of machine learning, data science, and modern methods of artificial intelligence
- Practical experience with large language models, agentic AI systems, retrieval-augmented generation, prompting, model evaluation, or comparable applications is expressly desired
- Experience in developing data pipelines, interfaces, and modular software architectures, ideally for heterogeneous or sensitive data
- Interest in the local, secure, and resource-efficient deployment of AI models, including aspects of inference, hardware requirements, latency, and scalability
- Ability to document technical solutions in a traceable manner, systematically test them, and evaluate them scientifically
- Strong interest in interdisciplinary research at the interface of AI, software development, medicine, clinical implementation, and trustworthy AI
- Very good written and spoken English skills and German language skills, particularly for collaboration with clinical users
We offer:
at the Else Kröner Fresenius Center for Digital Health
Project Deciphering Metastasis with Multimodal Artificial Intelligence Foundation Models
The position is to be filled as soon as possible on a full-time or part-time basis for an initial fixed term of 24 months, with the option of extension and long-term collaboration. Remuneration is based on the provisions of the Collective Agreement for the Public Service of the Federal States (TV-L); placement in pay group E13 is possible subject to the fulfillment of personal requirements.
- Compensation: Based on the TV-L collective bargaining agreement in pay grade E13 , plus an annual bonus, provided you meet the personal requirements
- Vacation: 30 days of paid vacation
- Professional Development: Extensive internal and external training and development opportunities to continuously strengthen your skills
- Company Culture: We value transparency, team spirit, and shared success. Celebrate with us at company gatherings or sporting events like the Rewe Team Challenge
- Mobility: A subsidy for a job ticket to make your commute eco-friendly and convenient
- Health: We promote your well-being with modern fitness and exercise programs at our health center, mental health programs, and a comprehensive workplace health management program
- Shopping benefits: Take advantage of employee discounts at our clinic pharmacy, exclusive corporate benefits, and other shopping portals
- Work-life balance: Our Family Office supports you with questions regarding daycare spots, vacation programs, and caring for family members. We also offer flexible work arrangements
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