Data Scientist/Computational Scientist - Multimodal and Multi-Agent AI for Precision Oncology

Universitätsklinikum Heidelberg
Heidelberg, Germany
27 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Bioinformatics Health Informatics Clinical Data Repository Computational Biology Computer Programming Data Integration Python (Programming Language) Machine Learning Prometheus Multi-Agent Systems Deep Learning
+2 more
Model Validation Information Technology

Job description

  • Multimodal data integration and preprocessing: Harmonize and integrate longitudinal clinical data, imaging, pathology and outcome data
  • Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction, and integrate them into the PROMETHEUS multi-agent
  • Agent interaction, explainability and uncertainty: Contribute to architectures that combine specialized AI components, knowledge-based reasoning and clinically interpretable outputs; implement uncertainty estimation and human-in-the-loop mechanisms
  • Validation and clinical translation: Perform internal and external validation, benchmark against established risk models and assess clinical utility across European cohorts
  • Scientific communication and collaboration: Contribute to high-impact publications, presentations and close collaboration with clinicians, imaging experts and AI researchers in Heidelberg and across the European consortium

Requirements

  • Master’s degree or PhD in Data Science, Bioinformatics, Computer Science, Computational Biology, Biostatistics, Medical Informatics, Mathematics, Physics, or a related discipline
  • Excellent programming skills in Python and/or R
  • Strong expertise in machine learning, deep learning and predictive modelling
  • Experience working with multimodal, longitudinal or high-dimensional datasets
  • Interest in or experience with multi-agent systems, agent orchestration, transformers, multimodal fusion, knowledge-grounded AI or clinical decision support systems
  • Sound understanding of model validation, calibration and reproducible research practices
  • Experience in explainable AI, survival analysis, medical imaging or healthcare AI is considered an advantage
  • A proactive, independent and scientifically motivated mindset combined with excellent communication and collaboration skills in an interdisciplinary international environment

Benefits & conditions

  • Collectively agreed remuneration according to TV-L E13, attractive company pension scheme (VBL)
  • Opportunity to work within a prestigious European research consortium at the forefront of AI and precision oncology
  • Active role in developing novel AI technologies with direct relevance for clinical decision-making and patient care
  • Access to unique multicentre datasets and state-of-the-art research infrastructure
  • Close collaboration with internationally recognized experts in medicine, artificial intelligence and medical imaging
  • 30 days vacation
  • Sustainable travel: job ticket
  • Family-friendly working environment: cooperative arrangements for childcare, subsidy for child vacation care, advice for employees with relatives in need of care
  • Wide range of health, prevention and sports offers

About the company

Join the PROMETHEUS consortium, a four-year EIC Pathfinder project developing a next-generation multimodal, explainable and multi-agent AI system for prostate cancer diagnosis and treatment selection (https://cordis.europa.eu/project/id/101306686). The consortium brings together leading European partners with multicentre patient cohorts, including Heidelberg University Hospital, DKFZ, Erasmus MC Rotterdam, Radboudumc Nijmegen, Karolinska Institutet and Umeå University.Within the group of PD Dr. Magdalena Görtz (https://www.dkfz.de/en/multiparametric-methods-for-early-detection-of-prostate-cancer), you will lead the development of an AI agent for treatment selection that combines longitudinal clinical data, imaging, outcome prediction and knowledge-based reasoning. The goal is to move beyond isolated prediction models towards an integrated agentic system that can model patient trajectories, compare treatment strategies and generate transparent, clinically meaningful decision support.

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