Postdoctoral Position, Model-based Evolutionary Genomics Unit

The Okinawa Institute of Science and Technology Graduate University (OIST; see www.oist.jp) is a dynamic new graduate university of science and technology in Okinawa Prefecture, Japan. The university is located on 85 hectares of protected forestland overlooking beautiful shoreline and coral reefs. The campus is striking architecturally, and the facilities are outstanding (OIST campus video tour). There are no academic departments, which facilitates multidisciplinary research. Outstanding resources and equipment are provided and managed to encourage easy access and collaboration. English is the official language of the University, and the university research community is fully international, with more than 50 countries represented. OIST is rapidly gaining recognition in the worldwide academic community as a model for excellence in education and research.

Position Summary:

Join the Model-Based Evolutionary Genomics Unit for a postdoc at the intersection of computational and evolutionary biology. Our unit works to decode the complex patterns and processes that underpin the evolution of life across its myriad forms with a significant focus on reconstructing the Tree of Life and modeling the dependency between different levels of organization in biological systems. By employing probabilistic models and leveraging the power of machine learning, we aim to unravel the co-evolutionary dynamics that have shaped the Tree of Life, from the enigmatic origins of early life forms to the sophisticated structures of modern biological systems.

Current topics include:

• applying and developing phylogenetically-aware models of horizontal gene transfer in order to reconstruct eukaryogenesis and better understand gene transfer in eukaryotes;
• applying and developing molecular dating methods that use novel sources of timing information from transfers and correlations between phenotypes and ancient environments;
• reconstructing ancient phenotypes from genome-scale data using machine learning;
• adapting protein language models to reconstruct fitness landscapes and ancestral sequences;
• understanding somatic evolution in hierarchically organized tissues and across the Tree of Life from a theoretical standpoint (e.g., To what extent has tissue organization evolved to minimize somatic evolution in humans? Why do both plants and animals have stem cells?)    

This postdoctoral position offers the freedom to pursue your own research ideas and provides a platform for innovation and discovery in a unit that thrives on intellectual curiosity and interdisciplinary collaboration. The position includes funding to travel to international conferences as well as extended research visits to international collaborators, including Tom Williams at the University of Bristol, Anja Spang at NIOZ in the Netherlands, Phil Hugenholtz at the University of Queensland, and others, provided by the budget of the Model-based Evolutionary Genomics Unit.

Some recent papers of ours:

“A rooted phylogeny resolves early bacterial evolution” Coleman, Davin, Mahendrarajah, Szánthó, Spang, Hugenholtz*, Szöllősi*, Williams*
Science 2021
https://science.sciencemag.org/content/372/6542/eabe0511.abstract

"Divergent genomic trajectories predate the origin of animals and fungi" Ocaña-Pallarès, Williams, Bapteste, Tikhonenkov, Keeling, Szöllősi, Ruiz-Trillo
Nature 2022
https://www.nature.com/articles/s41586-022-05110-4

“Trade-off between reducing mutational accumulation and increasing commitment to differentiation determines tissue organization” Demeter, Derényi, Szöllősi
Nature Communications 2022
https://www.nature.com/articles/s41467-022-29004-1

“Compositionally Constrained Sites Drive Long-Branch Attraction ” Szánthó, Lartillot, Szöllősi*, Schrempf*
Systematic Biology 2023
https://doi.org/10.1093/sysbio/syad013

“AleRax: A tool for gene and species tree co-estimation and reconciliation under a probabilistic model of gene duplication, transfer, and loss” Morel, Williams, Stamatakis, Szöllősi
Bioinformatics 2024 (to appear)
https://doi.org/10.1101/2023.10.06.561091