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) which hosts >20,000 individual movement trajectories from >110 species worldwide, and (ii) the spatio-temporal dynamics of oceanographic conditions and fisheries. You will address the following objectives
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have found that microbial interactions shape the temporal dynamics of antimicrobial resistance (AMR) in the Arctic. Moreover, there is emerging evidence from terrestrial ecosystems that antibiotics and
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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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, 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