Firma en Digital.CSIC (*)
Centro o Instituto
CSIC - Centro de Estudios Avanzados de Blanes (CEAB)
Ecología Marina. Ecología molecular del bentos marino
Doctor contratado / Doctor
My research career focused on the analysis of diversity and evolution through the use of molecular methods, including three main axes that are intertwined: studies of evolutionary genetics of invertebrates, bioinformatics and applied sciences. I have carried out evolutionary genetics studies in different taxa, covering from ancient divergences to contemporary population genetics. I used phylogeography, phylogenetics and population genetics methods, including the implementation of bayesian phylogenetic inference and Approximate Bayesian Computation methods. Within this first axis, I have carried out research on the conservation genetics of lobsters, molecular epidemiology and DNA barcoding. After obtaining an MSc in Mathematical Research, I started a second axis of research focused on the use of bioinformatics and mathematical modelling for the study of evolutionary processes. I have developed several computer pipelines, with some being published and released for public use. Research projects as a “Juan de la Cierva” and “Marie Curie” postdoc have focused on applied science, both in molecular epidemiology and biological control. I have studied the population genomics of pathogenic bacteria and metagenomics of the microbiome, with the development of several pipelines for analyzing genetic diversity from complete genomes and next-generation sequencing (NGS) data. As a postdoctoral researcher at the ISA, I have used NGS methods for characterizing arthropods and associated bacteria relevant for Biological Control. During the last 5 years I have also supervised research projects carried out by final year undergraduates, MSc and PhD students. Despite working on different taxa throughout my career, I have always carried out an integrated research by using molecular markers to define species limits (using phylogenetic methods and coalescent theory) and characterizing hidden diversity (using bioinformatics and metagenomics).
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