PhD position in Computational genomics in Zürich

The at the Institute of Agricultural Sciences at ETH Zurich investigates DNA variation in individual animal genomes and at the population scale. The group applies state of the art technologies to sequence the genomes of farm animals with long and short reads, and applies bioinformatics and statistical genomics approaches to characterize trait-associated sequence variation. We offer a PhD position at the interface of computational and statistical genomics, and bioinformatics. 

Cattle are an interesting «model organism» to study the molecular-genetic underpinnings of complex traits and dieseases. Whole-genome sequencing and dense phenotyping data are availble for tens of thousands of individuals. These data enable powerful association testing to study the genetic architecture of complex traits and diseases. Moreover, the availability of a vast amount of omics data, an accurate functional annotation, and long read sequencing offer great opportunities to scrutinize trait-associated genomic regions. Integrating different types of data will eventually permit identifying causal trait nucleotides.

This project builds upon previous research conducted by the  that aimed at investigating the , and . Moreover, access to , , and from multiple haplotype-resolved assemblies will also make structural variation amenable to association testing. The Animal Genomics group also has excellent collaborations with national and international academic and industry partners to establish large and informative datsets that permit exciting research at the forefront of animal genomics. 

We are looking for an enthusiastic and highly-motivated candidate to perform genome-wide association studies between sequence variant genotypes and complex traits and diseases in large cohorts of cattle. You will impute sequence variant genotypes for more than 100'000 individuals from various breeds using a sequenced reference panel of several thousand cattle. You will then implement novel statistical methods for association testing that take into account various modes of inheritance. Comprehensive omics and long read sequencing data will be investigated to fine-map trait-associated regions. Eventually, these analyses will identify candidate causal variants that will be subjected to in-depth functional characterization.

This is a fully funded fixed term position for 4 years with an anticipated start date of October 1st, 2022 (negotiable). Most of the data required to conduct the research have already been collected, enabling also an earlier starting date of the project. You will join the  led by Hubert Pausch.

• Research interest in statistical genomics, computational biology, computational genomics, or animal genomics.
• Experience with a programming language (e.g., python, R) and basic working knowledge with high-performance computing clusters is required.
• A MSc degree in genetics, genomics, computational biology, bioinformatics, animal sciences or disciplines related to the PhD position.
• Affinity to computational genomics, bioinformatics or statistics is a pre-requisite.
• The writing of scientific papers and active participation in international conferences requires good knowledge of English.

An inspiring, supportive, and team-based research environment to facilitate seamless integration into an ambitious research project. Our team consists of a young and international group of researchers who share a common vision of contributing significantly to the highest-level academic research in the broad field of animal genomics. The team has an excellent track record of publishing in the field’s leading and key multidisciplinary journals. For a list of recent publications, see

Specifically, we offer

• integration into a young and dynamic research team with diverse backgrounds,
• own responsibility within a fully funded 4 year project,
• flexibility in designing and pursuing own research ideas within the scope of the overall project,
• collaboration with national and international partners,
• competitive salary conditions according .

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