Abstract This paper presents the participation of the Biomedical Informatics and Technologies group (BIT) from the University of Aveiro in the SYMPTEMIST task at BioCreative VIII, with a primary focus on biomedical entity recognition and normalization tasks. We leverage a transformer-based solution with MCRF for entity recognition and hybrid semantic search approach for the normalization. Both our methods achieved top-performing scores, especially, our best entity recognition submission achieved 0.7369 F1 (3.69 points above median), while our best submission for normalization achieved 0.5890 (5.90 points above median). Code to reproduce our submissions is available at https://github.com/ieeta-pt/BC8-SympTEMIST. This article is part of the Proceedings of the BioCreative VIII Challenge and Workshop: Curation and Evaluation in the era of Generative Models.