This is a match-making section for OHAMR Call for proposals 2026.
H - Human Health
E - Environmentcomputational biology; GWAS; laboratory evolution; machine learning
We have a solid track record in the application of computational methods to large microbial genomes collections, in particular using statistical genetics (GWAS) and machine learning for the prediction of pathogenicity/virulence and AMR. We also have carried out work in developing new approaches for genomic epidemiology. We also have the ability to run in-vitro laboratory evolution experiments with large throughput, and have used it to study resistance to antisense-oligonucleotides (next generation antimicrobials).
Either a project in which there is the need to leverage large genome collections and apply GWAS/machine learning to them, or to identify transmission chains through genomic epidemiology. We can also work with antisense-oligonucleotides in case a project would like to explore them as alternative treatments or adjuvants. Lastly, we can quickly run large scale laboratory evolution to test for mechanisms of resistance induced by treatments.
Submitted on 2026-01-12 08:04:17
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