About the project
The signal processing of non-target liquid chromatography and gas chromatography high-resolution mass spectrometry (LC/GC-HRMS) data is extremely challenging. This project creates advanced transformer-based workflows to tackle this issue. By automating complex data workflows, this research significantly improves both chemical discovery speed and environmental health analysis accuracy.
The signal processing of non-target LC/GC-HRMS data is extremely challenging. Traditional heuristics struggle constantly with severe background noise, heavy peak overlap, complex feature alignment, and structural deconvolution across high-dimensional datasets.
This project creates advanced transformer-based workflows to tackle this issue head-on. The human exposome maps every lifetime environmental chemical exposure, making it crucial for understanding health and disease. However, data processing remains a massive bottleneck for researchers worldwide. To overcome these hurdles, the research leverages cutting-edge artificial intelligence, utilizing deep learning frameworks and transformer models tailored specifically for multi-dimensional data. Because transformers excel at recognizing complex patterns across retention time, mass-to-charge ratios, and signal intensities, they provide the ideal foundation for next-generation architecture.
By training self-supervised and generative models on massive spectral datasets, this project builds fully automated workflows for robust noise suppression, precise peak picking, and accurate fragment deconvolution. By automating these complex data workflows, this research significantly improves both chemical discovery speed and environmental health analysis accuracy. This interdisciplinary position bridges deep learning, computational mass spectrometry, and environmental health sciences. You'll design, validate, and apply these computational tools to large-scale biological samples, pushing the absolute boundaries of AI for science while solving critical, real-world environmental health challenges.
The School of Chemistry and Chemical Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
