17 beste Stellen als ai scientist in lausanne vd – aktuell auf Personalsuche

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Speechify —Lausanne, VD
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Research Scientist in computational comparative genomics, protein structure, and AI for crop improvement
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Research Scientist in computational comparative genomics, protein structure, and AI for crop improvement

SIB Swiss Institute of Bioinformatics -
Lausanne, VD

Jetzt bewerben

Details zur Stelle

Temporär
vor 5 Stunden

Qualifikationen

Doktorat
GitHub
Linux
Python

Vollständige Stellenbeschreibung

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.

Curious? Please click here to learn more about working at SIB.

To reinforce our team in Lausanne, Switzerland, we are seeking a

Job description

A Research Scientist position is available in the Comparative Genomics group of 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.

The Research Scientist will be expected to:

Develop methods to improve orthology reconstruction and HOG inference in plants.
Integrate protein structure information into the OMA orthology inference workflow, with the aim of improving deep homology detection and refining 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 a broad taxonomic sampling.
Evaluate methodological improvements using both curated gene family benchmarks and large-scale reference-free metrics, such as HOG completeness, ancestral gene repertoire size, duplication patterns, and consistency across taxonomic levels.
Explore how machine learning could be used to 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 GitHub repositories.
Prepare results for publication in peer-reviewed scientific journals.
Contribute to the broader Comparative QTLomics consortium, whose overall goal is to improve candidate gene prioritisation by combining AI-assisted QTL extraction from the literature, comparative evolutionary genomics, and functional data integration.

The exact methodological direction will be shaped together with the candidate, depending on their expertise. The successful candidate will work closely with an interdisciplinary team on the SNSF-funded project AI-driven Comparative QTLomics to accelerate crop improvement, which aims to integrate AI-assisted literature mining, orthology inference, comparative genomics, co-evolution, co-expression, and functional data to accelerate crop improvement across species. Collaborators include researchers at SIB, the University of Lausanne, Aarhus University, and Aalborg University.

Profile requirements

Applicants should have:

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.
A strong interest in method development for comparative genomics, evolutionary biology, or biological data integration.

Applicants should also have 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.

Experience with Linux, Python, HPC environments, and reproducible computational workflows is expected.

Prior experience with plant biology is desired but not required. We strongly encourage applications from candidates with transferable computational or methodological expertise from adjacent fields.

What we offer

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 UniProt and international plant genomics groups.
Possibility for international collaborations and research exchanges.
A flexible project with room for independent methodological development.

The position is hosted within the Comparative Genomics Group at SIB in Lausanne, Switzerland, which develops widely used comparative genomics resources and methods including OMA, FastOMA, OMArk, HOGProf, and related tools.

How to apply

SIB is committed to ensuring and fostering diversity and equal opportunities in the workplace as well as 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.

Apply

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