Aims and mission:

The coastal and marine environments are affected by oceanic phenomena (e.g., wind, wave, current). The practical and sustainable deployment of coastal and marine operations, including shipping, marine energy, fishing, offshore exploration, and coastal infrastructure development, relies heavily on accurate and reliable ocean forecasting. 

Improved forecasting capabilities are essential for the development and maintenance of marine energy operations, supporting the transition to a low-carbon economy and contributing to the attainment of the UN’s Sustainable Development Goals (SDGs).

Hence, our mission is to:

This Centre for Doctoral Training (CDT) has been funded by the University of Strathclyde in 2023.

Leadership Team:

Dr Bahareh Kamranzad

Lecturer & Chancellor’s Fellow

Civil & Environmental Engineering

Faculty of Engineering


Coastal Engineering, Ocean Climate change, Wave Modelling, Numerical and Machine Learning methods, Ocean Renewable Energy

Research Group:

Coastal Engineering & Ocean Climate (CEOC)

Dr Katherine Tant

Lecturer & Chancellor’s Fellow

Mathematics and Statistics

Faculty of Science


Modelling and simulation, wave propagation, optimisation, data analytics, signal processing, ML, Bayesian inference, imaging and inverse problems

Research Group:

Waves, Inverse Problems and Imaging (WiPi)

Dr Laibing Jia

Lecturer & Chancellor’s Fellow

Naval Architecture, Ocean & Marine Eng

Faculty of Engineering


Experimental mechanics, sensor and actuator technologies and offshore renewable energy

Meet the team:

Vicky Martí Barclay

Merlijn Surtel

Jack Lewis

Topic: AI-Based Approaches for Ocean Forecast and Development of Ensemble Ocean Climate Data

Bio: my background is in oceanography and marine renewable energy. Before coming to Strathclyde, I was a researcher in ocean renewable energy at Bangor University for nearly 3 years. I love being in (and on) the ocean doing water sports but out of season you’ll mostly find me in the forest doing orienteering.

External supervisors: 

Dr Lucy Bricheno (National Oceanography Centre) and Dr James Herterich (University College Dublin)

Topic: Physics Informed Machine Learning for Ocean Forecasting

Bio: I'm a computer scientist interested in machine learning and the mathematics behind it. Novel techniques for machine learning particularly intrigue me.

Research Interests: Machine Learning, Bayesian Deep Learning, Explainable AI, Modelling, Algorithms

Industrial Supervisor:

 Dr Valerie Lavinia (NPL)

Topic: Optimisation of Offshore Wind Farm Placement and Operation Using AI and Ocean Forecasting

Bio: I have a background in marine renewable energy with a particular focus on Tidal and Wind energy and the generating forces behind them. 

I have previously worked as a researcher at Bangor university using our research outputs to collaborate with industry and progress the MRE sector. I enjoy surfing, climbing and mountaineering in my free time