Postdoc – Bioinformatics / Computational Biology

Reference number: 2026-0007

• Heidelberg
• Full-time
• Signaling and Functional Genomics

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.

In collaboration with the Department of Molecular Human Genetics at Heidelberg University, the Division of Signaling and Functional Genomics at the German Cancer Research Center is seeking as soon as possible a

The Division of Signaling and Functional Genomics, led by Prof Dr. Michael Boutros, is pioneering novel methods for high-throughput genetic and pharmacological perturbations to systematically analyze signaling pathways and gene-drug interactions. In order to decipher the genetic and gene-drug interactions, we use model systems like Drosophila, human cell cultures and patient-derived organoids in high-throughput screens (e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications for RNAi and CRISPR reagent identification and off-target prediction (E-RNAi, E-CRISP), and public databases of functional screening data (GenomeRNAi, GenomeCRISPR), which are also part of the de.NBI and ELIXIR service portfolio. Further projects in the group are based on integrative analysis of multi-omics data such as RNASeq (bulk or single-cell) and long-read genomics data.

Your Tasks

Are you passionate about bioinformatics and eager to work in an interdisciplinary team? In our group, you will work on the following tasks:

• Develop and adapt bioinformatics approaches for experimental design, data analysis, and integration of genomic data sets
• Design the concept of analysis workflows for high-throughput screens and integrative analysis
• Maintain and further develop our software tools and databases for high-throughput screening experiments and add new content based on literature
• Support the group in analyzing multi-omics data, such as RNASeq and images
• Analyze and present the results
• Collaborate with internal and international collaboration partners
• Support and administer internal web and Linux servers

Your Profile

• Academic degree (master's and Ph.D.) with a background in computer science, bioinformatics, physics, or related field
• Proven experience in omics data analysis
• Knowledge of Bioconductor/R for biological data analysis
• Experience in object-oriented software development and building of modern web-based applications
• Good analytical and (bio)statistical skills
• Knowledge of relevant programming languages such as Java, Python, and Perl
• Good knowledge of relational and document-oriented database design (e.g., MySQL, MongoDB)
• Experience with Git and Docker containers
• Knowledge of Django and workflow management systems (e.g., Snakemake or Nextflow) is beneficial
• Proficiency in applying artificial intelligence methods to biomedical data is a plus
• Experience in image processing techniques, such as segmentation, is of advantage
• Ability to quickly grasp new concepts and work independently on complex problems
• Ability to work as part of an interdisciplinary team but also independently
• Excellent communication, organizational, and interpersonal skills
• An excellent written and oral command of English is essential

The application shall include a CV, a letter of motivation, degree certificates, and names and contact information of at least two references.

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