microbiology, genomics, proteomics, and AI-driven analysis to distinguish between true treatment failures, environmental ingress, and stress-induced recovery of bacteria. The goal is to improve understanding of bacterial survival in water systems and develop tools that support faster, more reliable decision-making for utilities.

Safe and resilient drinking-water supplies depend on understanding how bacteria respond to treatment and persist within complex infrastructure. Coliforms are important warning organisms, but a positive result does not reveal whether bacteria survived disinfection, entered through a network fault, regrew within a biofilm or recovered after sub-lethal stress. Resolving this uncertainty is a timely challenge at the intersection of public-health microbiology, water engineering, engineering biology and data science.

This PhD will develop an engineering-biology-enabled approach to classify stressed coliforms and support rapid incident triage. The student will combine controlled stress-and-recovery experiments with MALDI-TOF mass spectrometry, genome and plasmid sequencing, and interpretable machine learning to identify markers of injury, persistence and recovery. The most informative markers will be used to create a validated decision-support framework and a contained cell-free proof-of-concept biosensor for WRc and water-sector laboratories.

microbiology, genomics, proteomics, and AI-driven analysis to distinguish between true treatment failures, environmental ingress, and stress-induced recovery of bacteria. The goal is to improve understanding of bacterial survival in water systems and develop tools that support faster, more reliable decision-making for utilities.

Safe and resilient drinking-water supplies depend on understanding how bacteria respond to treatment and persist within complex infrastructure. Coliforms are important warning organisms, but a positive result does not reveal whether bacteria survived disinfection, entered through a network fault, regrew within a biofilm or recovered after sub-lethal stress. Resolving this uncertainty is a timely challenge at the intersection of public-health microbiology, water engineering, engineering biology and data science.

This PhD will develop an engineering-biology-enabled approach to classify stressed coliforms and support rapid incident triage. The student will combine controlled stress-and-recovery experiments with MALDI-TOF mass spectrometry, genome and plasmid sequencing, and interpretable machine learning to identify markers of injury, persistence and recovery. The most informative markers will be used to create a validated decision-support framework and a contained cell-free proof-of-concept biosensor for WRc and water-sector laboratories.