This position is focused in bioinformatics with a specific focus on phylogenetics and comparative structural biology and the successful candidate will be shared between both Dessimoz and Beltrao labs. Depending on personal circumstances, the successful candidate will be primarily based either at the University of Lausanne or at ETH Zurich, but will be affiliated with both institutions, as well as with the Swiss Institute of Bioinformatics.
The aim of this project is to overcome existing limitations in understanding the biological processes and evolutionary trajectory of the urmetazoan, the last common ancestor of all animals, through the reconstruction of its genome and that of its unicellular ancestor using innovative computational approaches. By reconstructing ancestral genomes, identifying co-evolving genes, understanding genetic changes that drove innovation and adaptation, and reconstructing protein interaction networks, with a specific focus on membrane trafficking and cytoskeletal organisation, we seek to gain valuable insights into the success of the urmetazoan and establish a solid foundation for reconstructing other ancestral clades.
Our interdisciplinary approach combines various fields including high-performance computing, bioinformatics, proteomics, biophysics, and cell biology. To begin, we will develop novel algorithms capable of integrating an unprecedented volume of genomic and proteomic data, enabling us to computationally infer ancestral proteomic sequences, folds, and interactions. Utilising this advanced toolbox of large-scale, fine-grained reconstructive techniques, we will then conduct experimental assessments on key cellular changes that have occurred in different organisms spanning the emergence of animals. We will reconstruct ancient proteins and investigate their interactions through biochemical, structural, and in vivo studies.
- Ph.D. in Structural Biology, Bioinformatics, Computational Biology, Applied Maths, or a related field.
- Strong background and expertise in at least two of the following areas: bioinformatics, phylogenetics, structural biology, and machine learning, with a demonstrated track record of research productivity in this field.
- Proficiency in computational analysis of large-scale structure data, including the use of bioinformatics tools, programming languages (e.g., Python, R), and statistical techniques.
- Familiarity with structural databases, tools, and resources.
How to apply: