Ana Rita Guimarães completed her PhD in Biomedicine at the University of Aveiro in 2023, specializing in the evolution of the genetic code and codon reassignment using Saccharomyces cerevisiae as a model organism. She has a strong foundation in Biology, holding a BSc and MSc in Molecular and Cellular Biology from the same university. Her early research, during a BSc internship at the Faculty of Medicine, University of Porto, focused on the cardiovascular effects of apelin in pulmonary hypertension models. For her MSc thesis, she joined the RNA Biology laboratory under Professor Manuel Santos, where she studied how codon ambiguities could lead to codon reassignment in S. cerevisiae. This research sparked her interest in genetic codes, leading to her involvement in projects on antifungal resistance and mistranslation in Candida albicans before her PhD. During her doctoral research, she became proficient in a range of laboratory techniques, including CRISPR cloning, experimental evolution, and mistranslation assessment using advanced reporters. She also developed extensive bioinformatics expertise, particularly in whole-genome and transcriptomic analysis. Currently, she is a researcher at iBiMED's Bioinformatics Lab, where she studies the genetic and epigenetic variome of clinical Candida albicans isolates. Her work focuses on analyzing large-scale sequencing data to identify genetic variants associated with fungal adaptation and virulence, supporting the project's aim to implement Personalized Medicine approaches for fungal infections. She also collaborates on multiple NGS-based projects, manages HPC resources and bioinformatics pipelines, and provides support to other researchers. She has shared her research at several national and international conferences, contributed to six publications, and is actively involved in science outreach activities such as the Junior/Summer Academy, Xperimenta UA, and educational sessions for secondary school students. Additionally, she leads an MSc course in Bioinformatics Data Analysis, where she teaches computational approaches for biological data interpretation.