Nuno Barbosa-Morais graduated in Physics Engineering from Instituto Superior Técnico and holds a PhD in Biomedical Sciences from the Lisbon School of Medicine (2007). Most of the PhD research took place at the University of Cambridge, UK, with Samuel Aparicio, and involved bioinformatics studies on the complexity of splicing and gene expression. He stayed in Cambridge for a postdoc at the Computational Biology Group, led by Simon Tavaré and based at the CRUK Cambridge Institute (2006-2010), where his main research was focused on understanding the complexity of gene expression regulation and its impact on disease mechanisms, namely oncogenesis. Nuno joined Ben Blencowe’ Lab at the University of Toronto in 2010 as an awardee of a Postdoctoral Fellowship from the Canadian Institutes of Health Research and a Marie Curie International Outgoing Fellowship (IOF). There he was involved in the analysis of mRNA-seq data for the inference of tissue and species specific alternative splicing patterns. He moved back to Lisbon in 2013 for the return phase of his IOF. He was awarded an EMBO Installation Grant and an FCT Investigator Grant to establish the Disease Transcriptomics Lab at the Institute of Molecular Medicine (now GIMM) in 2015, which aims at understanding how transcriptional changes in human tissues increase proneness to disease, using bioinformatic analyses of genomes and transcriptomes. Nuno received the University of Lisbon / Caixa Geral de Depósitos 2024 Scientific Award in Biomedical Sciences. Nuno is an Invited Associate Professor at the Lisbon School of Medicine, where he teaches Bioinformatics to Masters in Biomedical Engineering, Oncobiology and Biomedical Research. He is the coordinator of BIOMICS, a European Commission-funded Twinning project to locally foster excellent research, training and innovation in Biomedical Data Science. Nuno seeks to contribute to greater reproducibility in scientific practice by promoting interdisciplinary research, quantitative training for biomedical scientists and critical thinking in analyses of large molecular and clinical data.
