đź“…Date: 7-8 July 2026
đź“ŤLocation: National Oceanography Centre, Southampton, UK (In-person & Remote Options)
⚒️Organized by: BAS, ECMWF, Met Office, NCAS and NOC

🔎Contact: atb299 at noc.ac.uk for science/conference questions, NOC events for admin “behind the scenes” questions.

About the Workshop

We are excited to announce the second Machine Learning for Ocean Modelling Workshop, following our successful first edition last year. We are bringing together researchers to explore the evolving role of AI and ML in ocean science, share advancements, and discuss future directions. The workshop will feature keynote talks, short presentations, posters and practical sessions on key themes in the crossover between ocean modelling and ML. The event aims to foster collaboration, map UK Ocean AI activities, and identify challenges and opportunities in this rapidly growing field.

The content on these webpages for this year’s meeting are under development, and may change over time. You can review last year’s information on the 2025 pages.

The National Oceanography Centre (NOC) in Southampton is located on the docks, and is well located with easy road, rail and air connections to the rest of the UK and beyond. There are a range of hotels in easy reach of NOC at prices to suit the budgets of most attendees.

Themes & Sessions

Main sessions:

ML for analysis and downstream applications

This session will explore how ML can enhance the analysis, postprocessing, and downstream applications of ocean model outputs. Topics may include bias correction, feature extraction, uncertainty quantification, and linking model outputs to decision-making processes in climate science, marine ecosystem, and operational oceanography.

ML for observations, modelling and data assimilation

This session will cover AI applications in ocean observing systems and predictive modelling, including AI-driven Observation System Simulation Experiments (OSSEs), data fusion, data assimilation, and real-time forecasting. It will also address how AI can enhance ocean monitoring and improve the representation of physical and biogeochemical processes in both models and observational frameworks.

Integrating ocean processes and hybrid physics-ML modelling

This session will explore methods to embed process-based constraints, conservation laws, and ocean dynamics into machine learning models, as well as hybrid approaches that combine machine learning with traditional numerical ocean models. Topics may include physical-informed ML, ML-driven parameterisation, model correction, and surrogate modelling techniques to enhance both accuracy and computational efficiency.

Practical sessions:

These are designed to be interactive, allowing us all to play around with something that might be new to us.

  • Anemoi demo
  • Hybrid ML
  • Introduction to ML

Breakout groups:

  • Topics to follow, based on suggestions from registration

Workshop outcomes

  • Mapping UK Ocean AI activities across academia, research centres, and industry.
  • Position paper articulating key challenges in Ocean AI.
  • Network building for future collaborations and funding proposals.

Call for Participation

We invite researchers, students, industry professionals, and policymakers to contribute through talks, posters, and discussions.

📢 Abstract submission & registration

Apply here.