People > Dr Jozef Skakala

Dr Jozef Skakala

Ecosystem modeller

Contact Details


Jozef Skakala works at PML in the area of ecosystem modelling and biogeochemical data assimilation.

Jozef’s background (at all levels, from an undergraduate to PhD) is in theoretical physics, with PhD in the area of General Theory of Relativity (Victoria University in Wellington, New Zealand, 2011). This was followed by postdoctoral work (2011-2014) in theoretical astrophysics and fundamental physics in Brazil and India, where Jozef combined his interest in fundamental science with his desire to gain experience with life within diverse cultures in the developing world. In 2014 Jozef followed his long-term fascination with complex systems and his concern about environmental issues, and started to work in marine science at PML, from 2016 as a part of MEMP group.

Jozef’s research typically combines his mathematical background with a desire to understand complex ecosystem functioning. Most of Jozef’s more recent work was focused on biogeochemical data assimilation on the North-West European Shelf, with implications for operational forecasting and ``smart’’ observing systems. Jozef has also worked in applying (multi-)fractals to improve understanding of scale-relationships in oceanographic fields and to assess the skill of oceanographic models. A large part of Jozef’s experience is in development of low-complexity models to emulate the real world highly complex dynamics. Jozef focuses on developing techniques to understand and reduce the complexity of our current ecosystem models (e.g. using complex networks) and emulate those models with both AI/machine learning techniques and deterministic emulators. The purpose of this work is, among others, to improve the computational efficiency of our models, improve model parametrization, provide us with model uncertainty estimates and improve our understanding of ecosystem emergent phenomena.


SEAMLESS: Services based on Ecosystem data AssiMiLation: Essential Science and Solutions (SEAMLESS)

Contact: Dr Stefano Ciavatta

SEAMLESS aims at improving the current European capability to simulate and predict the state of marine ecosystems. The project is led by PML...

Combining Autonomous observations and Models for Predicting and Understanding Shelf seas

Contact: Dr Stefano Ciavatta

CAMPUS is a three-year project (2018–2021), funded by the Natural Environment Research Council, combining state-of-the-art computer modelling...

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Other key projects

  • SEAMLESS: Services based on Ecosystem data AssiMiLation: Essential Science and Solutions