This project aims to develop an AI-enhanced autonomous underwater vehicle (AUV) system designed to track Steel Catenary Risers (SCRs) in real time. Accurate tracking of SCRs is critical for maintaining the integrity of subsea infrastructure in the offshore oil and gas industry. The AUV will leverage advanced computer vision and machine learning techniques to continuously […]

Our project addresses a critical gap in maritime training, specifically the challenge of developing effective ice management skills in extreme environments. Traditional training methods rely heavily on human instructors, whose expertise is both scarce and costly, limiting the scalability and consistency of training programs. This issue is especially significant in industries like oil and gas, […]

AquaFishSense addresses the pressing issue of outdated and insufficient information available to fisheries, which impedes their ability to operate sustainably and efficiently. Fisheries face significant challenges due to the lack of real-time data on fish populations and aquatic habitats. This gap in information leads to overexploitation of marine resources, with approximately 34% of global fish […]

Kelp forests in the oceans provide considerable ecological benefits but are threatened by global warming, pollution, and human activities. This project aims to monitor kelp forests based on satellite data and artificial intelligence methods, beneficial for marine environmental conservation and climate change mitigation.

My research project will be investigating the effects of climate change, through ocean acidification and warming, on the growth rate of a coralline algae species that is prevalent in arctic and subarctic oceans. Coralline algae take in calcium carbonate from seawater and form hard skeletons, similar to a coral reef. In doing so, they create […]

The proposed project focuses on developing advanced AI technologies to enhance the partner organization’s platform. The intern will work on integrating and optimizing AI-driven solutions, such as personalized nutrition analysis and professional-client data sharing, using tools like OpenAI’s models and Retrieval Augmented Generation (RAG). By strengthening the AI workflows, including speech recognition and data processing, […]

We will investigate the impacts of wildfires on natural nanoparticles (NPs) in soil and water in two different regions: central Newfoundland, Canada, and the Landes de Gascogne forest, France. The Landes de Gascogne forest experienced significant wildfires in 2022, particularly impacting two major sites: “La Teste de Buch” and “Landiras.” The Landes region is characterized […]

The proposed project is related to analysis and optimization of a system of potential interest to oil and gas industry in terms of oil-spill remediation. A fixed wing aircraft can be retrofitted with an oil dispersant system suitable for fast response in case of large oil-spills. This oil dispersant system is to satisfy certain requirements […]

C-CORE and Memorial University have teamed to supervised the development of a 3U CubeSat called MUNStar-1. The CubeSat is an earth observation satellite containing a scientific payload for the measurement of Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) signals. The satellite will measure a number of ocean parameters to help understand climate change impacts, particularly […]

UNESCO Global Geoparks are a comparatively new designation with the focus being clearly on protection of Geoheritage, but also its promotion in the form of Geotourism Geoeducation and sustainable development. Since 2015 some 213 geoparks have been designated including one in Newfoundland Discovery UNESCO Global Geopark, which is based around the fact that it is […]

This project proposal aims to tackle a significant challenge in fintech: extracting human-readable descriptions from tabular financial data. Interpreting vast amounts of structured data in the dynamic financial technology landscape is crucial for informed decision-making and compliance reporting. The project seeks to develop a robust system by leveraging open-source Large Language Models (LLMs) tailored to […]

Check fraud continues to be a substantial challenge in the financial sector, involving the unauthorized use of checks for illegal fund acquisition. To address the issue of fraud detection, specific Artificial Intelligence models, particularly Convolutional Neural Networks (CNNs), have been increasingly applied to classify checks as fraudulent or legitimate. However, there is a crucial aspect […]