Research Scientist in computational comparative genomics, protein structure, and AI crop im

The SIB Swiss Institute of Bioinformatics is an internationally recognized non-profit organization, dedicated to biological and biomedical data science. Its data scientists are passionate about creating knowledge and solving complex questions in many fields, from biodiversity and evolution to medicine. They provide essential databases and software platforms as well as bioinformatics expertise and services to academic, clinical, and industry groups. SIB federates the Swiss bioinformatics community of some 900 scientists, encouraging collaboration and knowledge sharing.

The Institute contributes to keeping Switzerland at the forefront of innovation by fostering progress in biological research and enhancing health.

Location:

Lausanne, Switzerland. This position is part of the Comparative Genomics Group, working with Dr Natasha Glover and Prof Christophe Dessimoz at the SIB Swiss Institute of Bioinformatics / University of Lausanne.

Plant genomes present major challenges for orthology inference, including high duplication rates, gene family expansion, genome rearrangements, hybridisation, introgression, and variable annotation quality. The successful candidate will develop and benchmark new approaches to improve HOG inference by integrating additional sources of evidence, especially protein structure and synteny.

• Develop methods to improve orthology reconstruction and HOG inference in plants.
• Integrate protein structure information into the OMA orthology inference workflow, aiming to improve deep homology detection and refine HOGs.
• Perform large-scale orthology inference and benchmarking on plant datasets, including genome collection, quality control, species tree inference, and FastOMA-based HOG reconstruction.
• Build or assemble benchmark datasets of curated plant gene families, including cases with duplications and broad taxonomic sampling.
• Evaluate methodological improvements using both curated gene family benchmarks and large-scale reference-free metrics (e.g., HOG completeness, ancestral gene repertoire size, duplication patterns, and consistency across taxonomic levels).
• Explore how machine learning could combine sequence, structure, synteny, and phylogenetic information for improved homology and orthology inference.
• Develop reusable, open-source software and workflows to be made available through the group’s Git Hub repositories.
• Prepare results for publication in peer-reviewed scientific journals.
• Contribute to the broader Comparative QTLomics consortium, which aims to improve candidate gene prioritisation by combining AI-assisted QTL extraction, comparative evolutionary genomics, and functional data integration.

• A PhD in computational biology, bioinformatics, evolutionary genomics, structural bioinformatics, computer science, or a related field.
• Strong programming skills, preferably in Python.
• Experience working in Linux and HPC environments.
• Experience with reproducible computational workflows and large-scale biological datasets.
• Strong interest in method development for comparative genomics, evolutionary biology, or biological data integration.

• Expertise in one or more of the following areas:
  Comparative genomics or evolutionary genomics;
  Orthology inference or gene family reconstruction;
  Structural bioinformatics or protein structure analysis;
  Synteny analysis or genome evolution;
  Phylogenetics or phylogenomics;
  Machine learning or AI applied to biological data.
• Prior experience with plant biology is desired but not required. Transferable computational or methodological expertise from adjacent fields is encouraged.

• An interdisciplinary and collaborative research environment at SIB and UNIL.
• The opportunity to develop new methods in comparative genomics, orthology inference, and AI-assisted biological data integration.
• Access to large-scale genomic, phylogenomic, and protein structure datasets.
• Collaboration opportunities with Uni Prot and international plant genomics groups.
• Possibility for international collaborations and research exchanges.
• A flexible project with room for independent methodological development.

SIB is committed to ensuring and fostering diversity and equal opportunities in the workplace and in the scientific ecosystem. If you are interested in this challenging and highly interesting position, please submit your application including CV, letter of motivation, and contact details for 2–3 referees through our online portal by clicking the “Apply” button.