Development of an AI-Integrated electrochemical-plasmonic biosensor for detecting AMR metabolic signatures

Scientific Background: Antimicrobial resistance (AMR) is one of the most pressing global health challenges, threatening the effectiveness of antibiotics and the treatment of infectious diseases. This cutting-edge project focuses on developing a groundbreaking multimodal biosensing platform that integrates electrochemical and plasmonic technologies with Artificial Intelligence (AI) analysis to monitor real-time metabolic changes in bacteria. This approach will uncover metabolic fingerprints associated with resistance, identify biomarkers, and facilitate the discovery of novel antibiotics.

Research methodology: As a PhD student, you will work with a transdisciplinary team of experts at the School of Biological Sciences and the School of Electronics, Electrical Engineering and Computer Science at Queens University Belfast and at the School of Engineering at Ulster University. You will design and build a multimodal biosensor capable of detecting metabolic shifts in bacterial strains exposed to antibiotics. Working with an established library of AMR-related bacterial strains, you will combine advanced electrochemical and plasmonic sensing techniques to measure dynamic metabolic changes. You will apply machine learning algorithms to classify and interpret complex biosensor data, identifying resistance biomarkers and exploring how these can inform new therapeutic strategies. This project involves both laboratory-based experiments and computational analysis, offering a balanced and diverse research experience.

Training: You will gain hands-on experience in sensor technology development, advanced microscopy, machine learning, and bioinformatics. Training will also cover bacterial culture techniques, antibiotic testing, and data science tools, including Python-based AI algorithms like LightGBM and convolutional neural networks. Collaborations with multidisciplinary teams will provide you with opportunities to develop transferable skills in teamwork, communication, and project management. The training environment will prepare you for future roles in academia, industry, or policy-focused research.

This innovative project, combining real-time biosensing and AI, offers a unique opportunity to contribute to global efforts to combat AMR, with potential clinical applications and pathways for expanding the technology to other pathogens and drug classes. 

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