Higher Degree by Research Application Portal

Marine debris poses a critical threat to ocean ecosystems, yet our understanding of its distribution and dynamics remains limited. While land-based surveys and beach clean-ups provide valuable data, they capture only a fraction of the problem. Very little data is available on debris floating in the sea; thus, existing coastal surveys almost certainly underrepresent the actual quantity and distribution patterns of oceanic debris. Once introduced into the ocean, some debris will likely never drift ashore due to ocean circulation patterns and instead will concentrate in coastal and oceanic garbage patches. Floating marine debris, including those coming from fishing activities (e.g., ghost nets), disrupt and damage ecosystems (e.g., through physical entanglement on the reef), contaminate food chains, can be a safety issue, especially for vessels, and contribute to climate change through greenhouse gas emissions. Traditional hydrodynamic models lack real-time tracking capabilities, creating a critical knowledge gap in understanding marine debris movement and accumulation patterns. This limitation hampers effective mitigation strategies and policy development for marine pollution management in the Indian Ocean region.  Using remote sensing data to develop and validate novel rapid approaches for floating marine debris at sea could be used to determine their drift in real-time or with a relatively short lag (e.g., days/weeks), representing a significant improvement compared to hydrodynamic models.

 Aims:

 Develop and validate novel approaches combining satellite and drone imagery with machine learning and artificial intelligence algorithms to detect and track floating marine debris in near real-time.Identify environmental drivers of debris accumulation and movement.Create predictive models for marine debris movement incorporating oceanographic and meteorological data.Support evidence-based decision-making for coastal and marine pollution management.

Skills:

GIS and spatial data analysis (QGIS, ESRI); Remote Sensing and image analysis (preferred); Spatial or Environmental modelling; Programming in R or Python; Strong written communication and scientific writing skills

UWA eligibility requirements for PhD candidates

Desired Skills
• GIS and spatial data analysis (QGIS, ESRI)
• Remote Sensing and image analysis (preferred)
• Spatial or Environmental modelling
• Programming in R or Python
• Strong written communication and scientific writing skills

Send copies of CV, degree certificates, transcripts, language test results (for international candidates) and cover letter outlining your relevant expertise and motivation for this PhD project to sharyn.hickey@uwa.edu.au and r.galaiduk@aims.gov.au

Ronen Galaiduk: r.galaiduk@aims.gov.au

Sharyn Hickey: sharyn.hickey@uwa.edu.au