Data services company develop new advances helping the maritime industry reach net zero

Teesside University is working with SeerBI, an award-winning data science company based in the Launchpad in Victoria Building, Teesside University

The Net Zero Industry Innovation Campus (NZIIC) run by Teesside University, and funded by the European Regional Development Fund (ERDF)*, aims to support Tees Valley SMEs to develop net zero capabilities and opportunities.

The project seeks to develop novel machine learning solutions to the emissions crisis the maritime sector currently faces. These solutions will be aimed at decreasing the harmful CO2 emissions put out by ports worldwide through better analysis methods along with innovative prediction and prevention technology.

The solutions will be in line with the UK governments Maritime 2050 strategy that aims to make the UK a global leader in the maritime sector and promote clean maritime growth.

SeerBI is an award-winning data science company currently focused on the transport sector, specifically maritime. They provide data science services ranging from data visualisation to bespoke machine learning and AI development for maritime operators.

The project will work with UK maritime stakeholders to test and develop the research as the project progresses. SeerBI will then seek external funding to take the project forward from the research into a real-world application to be applied in ports around the globe.

The collaboration will output novel machine learning algorithms and data analysis methods focused on the analysis and prediction of CO2 output specifically in ports. This analysis and prediction will serve to help ports better understand their environmental impact and what they can do to prevent future damage.

Owain Brennan, Founder and Managing Director of SeerBI, said, “we are very excited to begin this research opportunity with Teesside University and do our part in making Net Zero in maritime a reality, a goal our entire team is excited about.

“The value of machine learning and analytic capabilities have already been well researched and applied in manufacturing and many engineering disciplines, but it is maritime’s turn to take the stage”

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