<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-07-08T08:44:08Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/6CFFAFA6-BDD1-444A-AD5A-01A6817E9F77" ns1:id="6CFFAFA6-BDD1-444A-AD5A-01A6817E9F77"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/645D4515-467A-4AA6-A51A-AB2C8992C3F4" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/9E333064-2ED0-40E0-A075-FCB7192A0A88" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/9E333064-2ED0-40E0-A075-FCB7192A0A88" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/14B9575D-EE36-4E7E-B9BF-CC592545B085" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/BC4D0218-3234-4BC7-8B8D-728F4FB5F883" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2027-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/F6832C01-071C-4637-B199-A5D46F78A423" ns1:rel="FUND" ns1:start="2025-03-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10140520</ns2:identifier></ns2:identifiers><ns2:title>AMBROSIA: Autonomous Monitoring of Biodiversity with Remote Ocean Sensing and Integrated Analytics</ns2:title><ns2:status>Active</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>AMBROSIA is an innovative project designed to enhance the monitoring of marine biodiversity through an integrated and autonomous system. Combining static and mobile monitoring technologies, this project will deploy advanced sensors and autonomous vehicles to collect comprehensive data on marine ecosystems.

Key components of the system include hydrophones for acoustic monitoring, underwater cameras for species detection, multi-parameter sensors for environmental data, and eDNA samplers for genetic analysis of marine life. These elements will be integrated into a centralised platform capable of operating continuously and transmitting data wirelessly.

Data collected will be processed using advanced machine learning algorithms to provide real-time insights and detailed biodiversity assessments. Our innovative approach addresses current limitations in marine monitoring by offering a scalable, cost-effective, and non-intrusive solution that supports environmental conservation, regulatory compliance, and sustainable management of marine resources.</ns2:abstractText></ns2:project>