Data Scientist – Multi-Omics and Machine Learning for Prostate Cancer Early Detection

Reference number: 2026-0174

• Heidelberg
• Full-time
• Multiparametric Methods for Early Detection of Prostate Cancer

The German Cancer Research Center (DKFZ) is one of Europe’s largest cancer research centers. “Research for a life without cancer" is the mission of our world-class scientists and all our team members.

The DKFZ is a place where the brightest minds pursue bold ideas and seek answers to pioneering scientific questions through collaboration, innovation, and exploration across many disciplines. We provide a dynamic environment which empowers excellence with state-of-the-art technologies, cutting edge infrastructure, and a global scientific network. 

Contribute your knowledge, vision, and dedication to create a space where scientific discovery in cancer research is transformed into benefits for human health.

The Junior Clinical Cooperation Unit "Multiparametric Methods for Early Detection of Prostate Cancer" is seeking, starting at the earliest date possible, a

  Guided by our mission, “Research for a life without cancer,” we investigate the biological basis of cancer and develop innovative approaches for cancer prevention, early detection, diagnosis and treatment.
The Junior Clinical Cooperation Unit “Multiparametric Methods for Early Detection of Prostate Cancer”, led by PD Dr. med. Magdalena Görtz, operates at the interface of the DKFZ and the Department of Urology at Heidelberg University Hospital. Our interdisciplinary team integrates clinical research, medical imaging, biostatistics and machine learning to develop clinically relevant tools for individualized cancer detection and risk stratification.

Your Tasks

You will take a leading role in the computational analysis of multimodal prostate cancer cohorts, integrating clinical, proteomic, imaging and longitudinal data into clinically meaningful prediction models.

• Develop reproducible analysis pipelines in R and/or Python
• Process, harmonize and integrate high-dimensional multimodal datasets
• Apply statistical and machine-learning methods for biomarker discovery and risk prediction
• Perform rigorous model validation and assess clinical utility
• Interpret and visualize results with a focus on robustness, transparency and clinical relevance
• Contribute to high-quality publications and collaborate closely with clinical and scientific partners

Your Profile

We are looking for an ambitious scientist with excellent quantitative training, high scientific standards and the motivation to take substantial responsibility within an interdisciplinary research project.

• Master’s degree or doctorate in bioinformatics, data science, biostatistics, computational biology, computer science, mathematics, physics or a related field
• Excellent programming skills in R and/or Python
• Strong expertise in statistics, machine learning and high-dimensional data analysis
• Experience with feature selection, predictive modelling and robust validation strategies
• Independent, structured and quality-oriented working style
• Excellent English and strong communication skills
• Enthusiasm for interdisciplinary cancer research and clinical translation

Experience with omics data, multimodal integration, survival analysis or explainable machine learning is advantageous. Prior experience in prostate cancer research is not required. We particularly encourage applications from ambitious researchers who strive for methodological excellence and clinically meaningful impact.

We Offer

Are you interested?

 Then become part of the DKFZ and join us in contributing to a life without cancer!

 ​Duration: The position is initially limited to 2 years with the possibility of prolongation.

Application Deadline: September 1, 2026

Applications by e-mail cannot be accepted.

Please also note that we cannot return applications submitted by post.

 

We are convinced that an innovative research and working environment thrives on the diversity of its employees. Therefore, we welcome applications from talented people, regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical ability, religion and age. People with severe disabilities are given preference if they have the same aptitude.

Notice: We are subject to the regulations of the Infection Protection Act (IfSG). Therefore, all our employees must provide proof of immunity against measles.

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