Job description:

The project addresses the question why some organisms, including many humans, never develop cancer in their lifetime. To address this question, we use comparative genomics approaches involving two model organisms with vastly different cancer susceptibilities: mice (which readily develop tumors upon treatment with a chemical carcinogen) and naked mole-rats (which are naturally resistant to cancer). The goal is to untangle the molecular and cellular mechanisms driving natural cancer resistance and those involved in the earliest stages of cancer development.

You will be responsible for leading and performing analyses as well as building computational models at the intersection of comparative genomics, clonal evolution, and translational research.

The collaborative project interlinks two research groups at the German Cancer Research Center (Division of Regulatory Genomics and Cancer Evolution, Prof. Dr. Odom and Junior Research Group Somatic Evolution and Early Detection, Dr. Goncalves) and combines cutting-edge genomics technologies and computational methods with cell biology to understand early cancer development.

Working as part of a supportive and collaborative team, you will formulate and pursue translational scientific questions based on large genomics datasets. You will curate, manage and perform high quality data analysis and provide feedback to other computational and wet lab scientists.

Starting date is flexible but can be as early as possible.

Requirements:

Candidates must have a PhD in a field related to computational biology (e.g. genetics, bioinformatics, physics, engineering or applied statistics/mathematics) and a proven understanding and experience in the fields of genomics, data processing and high-throughput data analysis. A solid training in molecular and system-wide biology is required. Equally important is a high motivation to learn and adapt scientific challenges and to become a positive member of the labs. We use many different methodologies and concepts in different areas in biology. Therefore, an open mind set and a broad interest in biology across subfield boundaries is needed.

In addition to strong communication skills, you will be highly organized with a logical and diligent approach to information gathering.

 Other required skills:

• Excellent programming and data analysis skills in high-level scripting languages (R and/or Python)
• Demonstrated expertise in the management and analysis of very large data sets
• Experience in writing manuscripts for publication
• Excellent organizational skills
• Fluent in English and good written and oral communication skills