Postdoctoral position in complex trait genetics and computational genomics


 

 

Postdoctoral position in complex trait genetics and computational genomics

 

A postdoctoral position in complex trait genetics and computational genomics is available in the Chan Lab at the Max Planck Institute in Tübingen, Germany as part of a Max Planck and ERC-funded research team investigating gene regulatory network evolution in mouse and human tissue culture models.

 


The key innovation in the HybridMiX project is the development of in vitro recombination (IVR) in tissue culture, specifically in F1 interspecific hybrid mouse ES cells for genetic mapping (see Lazzarano et al., PNAS, 2018). IVR allows us to create recombinant cell lines across species of effectively unlimited panel size, at low costs and . We now aim to map the evolutionary divergence between mouse species at the tissue and cellular level by generating interspecific panels and obtaining their phenotypes via tissue engineering, organ-on-a-chip or droplet microfluidic single-cell methods.

Your role:  You will be leading our computational lead in analyzing large mapping datasets derived from genomics and functional assays from our IVR panels. You will map and identify genetic variation that contribute to differences between mouse species. You will also integrate such individual differences to changes in the gene regulatory network. You will have the opportunity to work with unique single-cell or single-molecule datasets to detect mitotic recombination events and connect such changes to the cell fates in cell lines or whole organisms. 

Requirements: You will have a PhD or equivalent degree in the areas of statistical, functional or quantitative genomics. You should have a strong background in analyzing genomic data and the proficiency to handle large datasets, (e.g. skills in Unix, R and scripting or programming languages). In our projects we routinely integrate information from diverse sources, including single-cell, linked-read, chromosome conformation capture, as well as image analysis to assist with our analyses of cellular and tissue-level phenotypes and genotypes. Passion for research, team spirit and enthusiasm are essential. English is required.

Our Team: We are a multidisciplinary team that focuses on the systems biology of development and evolution in mice, combining population and functional genomics with molecular biology and tissue engineering techniques to study the evolution of gene regulatory network in mouse and its close relatives. Our research group is funded by the European Research Council (ERC) and the Max Planck Society and is located on the Max Planck campus in Tübingen, Germany. The Max Planck Tübingen Campus is a highly innovative research hub with world-class genomics and machine learning expertise. Our sequencing core features the Illumina, PacBio and 10X Genomics platforms. English is the working language. All seminars and communications are in English.

Our Offer: The position is available for an initial 2 years with the possibility of extension based on performance. Salary and benefits are according to the German public service pay scale (TVöD Bund up to and including E13) and are commensurate with training and experience.

The Max Planck Society seeks to increase the number of women in areas where they are underrepresented, and therefore explicitly encourages women to apply. The Max Planck Society is committed to employing more handicapped individuals and especially encourages them to apply.

To Apply: Consideration of applications will begin on 1st Dec 2018. The projected start date is in early 2019 but can be negotiable. Please send your informal enquires or application to Dr Frank Chan at frank.chantue.mpg.de. 

Complete applications should include: 1. a statement of research interests and why you have applied for this position, 2. your CV, and 3. three reference letters

 

Publication: Lazzarano et al., Genetic mapping of species differences via in vitro crosses in mouse embryonic stem cells. Proc Nat Acad Sci, 2018. doi: 10.1073/pnas.1717474115