Sort by
Refine Your Search
-
Category
-
Field
-
network-based, graph modelling, dimensionality reduction or machine learning approaches You can process and derive novel insights from integrative analysis of multi-omic datasets Desirable but not required
-
, physics, mathematics or engineering. The programme particularly welcomes proposals aligned with the following research directions: • Mountain biodiversity genomics: understanding the genomic basis
-
these questions, we develop mathematical and computational approaches to estimate mutation probabilities and selection. Tumor mutations are caused by diverse mutational processes, which can be identified through
-
are influenced by the influx of mutations and by selective pressures, as inferred from mutation data. To address these questions, we develop mathematical and computational approaches to estimate mutation