Postdoctoral Researcher - Computational & Translational Analysis of Pulmonary Nodules

Postdoctoral Researcher - Computational & Translational Analysis of Pulmonary Nodules

Reference number: 2026-0095

• Heidelberg
• Full-time
• Computational and Molecular Prevention

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 Division of Computational and Molecular Prevention (headed by Prof. Dr. Filimon Goncalves) is seeking for the next possible date a

The Division of Computational and Molecular Prevention of the DKFZ, in collaboration with the Translational Molecular Imaging in Oncologic Therapy Monitoring Unit, invites applications for a postdoctoral researcher to join an interdisciplinary research program within the PRELUNG study, focusing on early lung cancer detection and biological characterization of pulmonary nodules.
This position combines cutting-edge multimodal data analysis with patient-centered translational research, including a novel co-design initiative on the return of molecular and imaging data to study participants.

Project Background:
Pulmonary nodules from hundreds of patients are being collected and biopsied as part of a prospective imaging study. Up to 40 FFPE biopsy samples will be analyzed using spatial transcriptomics and mutREAD DNA sequencing, enabling integrative analysis of spatial gene expression, tissue architecture, and genomic alterations at early stages of lung carcinogenesis. The postdoctoral researcher will play a central integrative role, leading unbiased computational analyzes, biological interpretation, and biomarker discovery in close collaboration with clinicians, pathologists, and technology experts across multiple research groups.

Your Tasks

Scientific & Computational Responsibilities:

• Lead unbiased, data-driven integration of:
• Spatial transcriptomics data
• Genomic alteration profiles (copy number alterations, SNVs)
• Histopathological diagnoses and clinical metadata
• Radiomic features (in collaboration with imaging specialists)
• Perform unsupervised clustering and pattern discovery without predefined diagnostic labels
• Interpret molecular and microenvironmental states of benign, premalignant, and malignant pulmonary nodules
• Identify and prioritize biomarkers for:
• Liquid biopsy development
• PET tracer target discovery
• Contribute to validation strategies (e.g. immunohistochemistry) in collaboration with pathologists
• Lead the preparation of high-impact, interdisciplinary publications

Translational & Collaborative Responsibilities:

• Act as a scientific integrator across multiple collaborating research groups
• Communicate effectively with clinicians, pathologists, imaging scientists, and molecular technologists
• Maintain reproducible and well-documented analysis workflows

Patient Engagement & Return-of-Data Work Package:

• Actively participate in a participant-driven co-design process to develop a framework for returning molecular and imaging data to study participants
• Contribute scientific content to patient workshops and plain-language communication materials
• Co-facilitate workshops together with ethicists, communication experts, and institutional partners
• Help synthesize participant feedback into structured outputs, including:
• A return-of-data blueprint
• Prototype participant reports
• An evaluated framework for future implementation

Your Profile

• PhD in computational biology, bioinformatics, systems biology, biomedical data science, cancer biology, or a related field
• Strong experience in omics data analysis, ideally including single-cell or spatial transcriptomics
• Proficiency in R and/or Python and modern data-analysis workflows
• Experience with unsupervised learning, clustering, and integrative analysis
• Ability to translate computational findings into meaningful biological and translational insights
• Strong interest in early cancer detection and biomarker discovery
• Exceptional interpersonal and communication skills
  • Excellent communication skills across diverse audiences (patients, clinicians, computational scientists)
  • Empathy, active listening, and comfort engaging with non-expert stakeholders
  • Ability to discuss uncertainty and non-actionable findings responsibly
  • Confidence contributing to group facilitation without reliance on hierarchical authority
  • A collaborative, inclusive, and respectful working style

Applicants are encouraged to highlight concrete evidence of these skills (e.g. patient-facing research, public engagement, teaching, facilitation, or interdisciplinary leadership).

You can look forwad to:

• Active involvement in innovative patient-centered research initiatives
• Close interaction with clinicians, pathologists, and technology developers
• Opportunity to lead high-impact publications

Application:

Interested applicants should submit the following documents via our online application tool:

• A cover letter describing your scientific interests and motivation for this interdisciplinary and patient-engaged role
• Curriculum vitae