Postdoc in computational genomics and transcriptional regulatory variation

University of Copenhagen
Job Location
Job Description

The Andersson lab at University of Copenhagen is looking to recruit a highly motivated and talented postdoctoral researcher to join our group working on computational biology with a strong focus on enhancers and transcriptional regulation. The ideal candidate should be comfortable in statistical learning techniques and computational analysis of large-scale sequencing data, independent and have a strong interest in transcription and transcriptional regulation.

The project will focus on systematically characterizing human regulatory variation and its consequences. We further aim to infer models describing the function of a regulatory element and its importance, and thus the impact on regulation upon disruption.

Read more about the position and apply online on the university job site. Deadline for applications is July 31, 2016.

The Andersson lab is based in a highly collaborative environment in the Bioinformatics Centre in the Section for Computational and RNA Biology, which is currently composed of 11 strong research groups. The Department of Biology is located at the Biocenter in central Copenhagen, Denmark. Read more about the Andersson lab at


We are seeking a highly motivated individual with a PhD in Bioinformatics, Computational Biology or in a related quantitative discipline, with a substantial publication record in high-quality international journals focused on the analysis of genomics data. Experiences with DNA or RNA sequencing data, genotyping data, statistical genetics, computational modeling, machine learning and/or statistical learning techniques are considered a major plus. Importantly, the candidate should have a strong interest in transcription and transcriptional regulation. 

Specifically, the candidate should meet the following requirements:

• A PhD in Bioinformatics, Computational Biology, or in a related quantitative field to be able to quickly acquire Bioinformatics computational skills
• Strong documented experience in analyzing high-throughput sequencing data
• Strong programming and data analytical skills
• Fluent English 

The following are not required but considered a plus:
• Documented expertise in statistical learning and/or machine learning techniques
• Documented experience in the analysis of functional genetic variation

How to Apply

Apply online on the university job site. Deadline for applications is July 31, 2016.

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