2 Postdoctoral and 2 PhD Student Positions for Mining Big Biological Data Generated by the International Cancer Genome Consortium (ICGC)
The major aim of these positions is the development and application of innovative computational methods for a deep analysis of high-throughput genomic, transcriptomic, and epigenomic data generated in house within the framework of the International Cancer Genome Consortium (ICGC). We are especially interested in bioinformatics analysis and biological interpretation of differentially expressed genes, alternative splicing, and alternative promoter usage, and in analyzing DNA modifications and chromatin states at regulatory DNA-elements located in the non-protein coding part of cancer genomes.
These positions will involve working in the Divisions of Molecular Genetics (headed by Prof. Peter Lichter, http://www.dkfz.de/en/genetics/) and Pediatric Neurooncology (headed by Prof. Stefan Pfister, www.pediatric-neurooncology.com) at the German Cancer Research Center (DKFZ), Germany’s largest biomedical research institute.
The candidates will be part of the Computational Cancer Genomics group headed by Dr. Marc Zapatka or of the Computational Oncoepigenomics group headed by Dr. Lukas Chavez. The teams work closely together, share methods and pipelines, and are very international and multidisciplinary. The groups are embedded in a large local community of bioinformaticians in Heidelberg and have access to a high-performance computational infrastructure. The working atmosphere is lively and friendly; working language is English.
The Computational Cancer Genomics group has successfully analyzed the genome and methylome of childhood brain tumors in the context of the first German ICGC project leading to numerous publications (e.g. Nature, Cell) and now focuses in addition on the analysis of the transcriptome and the non-coding part of the genome. Specific research interests include (but are not limited to):
• Expression of long coding and noncoding RNA as well as small RNA
• Cancer relevant and general patterns of alternative splicing
• Alternative promoter usage leading to novel cancer relevant transcription
• Regulation of alternative splicing analyzed by integrating (epi)genomic and transcriptomic sequencing data
The Computational Oncoepigenomics group aims to comprehensively understand the epigenetic diversity of childhood brain tumors with a specific focus on analyzing the cancer enhancer landscape and on deciphering the cancer histone code. Specific research interests include (but are not limited to):
• Comparative enhancer mapping and characterization of histone modification states across different types of pediatric brain tumors
• Analysis of genomic structural variants and their effects on enhancer activity and gene expression
• Integration of different levels of genomic and epigenomic data
• Identification of drug targets and drug matching for recurrent malignant childhood brain tumors based on high dimensional biological data to improve treatment decisions
Our groups have solid experiences in developing bioinformatics tools for high-throughput sequencing data analysis and we routinely apply our and other bioinformatics tools in the context of large-scale DNA genomic and epigenomic studies in health and disease.
For an application as Postdoctoral Scientist:
• PhD in computer science, mathematics, statistics, physics or bioinformatics with experience and/or desire to excel in a biological area
• Basic skills in statistics and programming
• Very good organization skills, motivation and proven ability to independently conduct research in a team
• Excellent communication and interpersonal skills, oral and written fluency in English
For an application as PhD student:
• MSc (or equivalent) in biology, biochemistry, physics or bioinformatics and research experience in a biological area
• Basic skills in statistics and programming and, ideally, of molecular biology
• High motivation and proven ability to conduct research independently
• Good communication skills, good knowledge and command of English.
Demonstrable skills in programming and biostatistics (e.g. Perl/Python, R/Matlab, machine learning) as well as the Unix computing environment (e.g. BASH, HPC usage) are essential. Experience in analyzing short read DNA sequencing data, ideally in the context of transcriptomes and/or epigenomes (i.e. DNA methylation, histone modifications, general DNA-protein interactions), is a plus.
- Interessanten, vielseitigen Arbeitsplatz
- Internationales Umfeld
- Vergütung nach TV-L
- Möglichkeiten zur Teilzeitarbeit
- Flexible Arbeitszeiten
- Gute Fort- und Weiterbildung
The Postdoctoral position is limited for 2 years and the PhD Student Position is limited for 3 years.
This opportunity has expired. It was originally published here: