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solve complex problems from theory to decision making, combining several disciplines (e.g. economics and applied sciences). A candidate with strong statistical/mathematical knowledge and experience
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introduced EU Deforestation Regulations aim to reduce the environmental impact of ‘Forest Risk Commodities’ (FRCs) such as soy, palm oil and coffee. Land-use changes, like forest clearing and burning
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health. You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem
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regions? Which weather, landscape and land use factors promote or inhibit megafire development? Has climate change increased megafire risk, and how could those risks evolve in the future? Methodology
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, visualisation and interpretation using coding (Python or Matlab) and learn to use a 1-dimensional ocean biogeochemical model. You will collaborate with the dynamic Rothera and POLOMINTS (http://polomints.ac.uk