17 postdoc-computational-fluid-dynamics PhD positions at University of East Anglia; in United Kingdom
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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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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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dynamics, and evolutionary adaptability compared to sexual reproduction in natural (wild) plant populations. We will address two questions: (1) Does apomixis act as a transitional “safety net” mechanism post
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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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, 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
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leading Developmental Science Research Group and Developmental Dynamics Laboratory Work with leading researchers in a collaborative and interdisciplinary environment Comprehensive training and support
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across different imaging devices, including future sensors with unknown spectral sensitivities. Training The student will be based at the Colour & Imaging Lab at the School of Computing Sciences which has
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(optional). Person specification: Prior experience in computer coding (e.g., Python, SLiM), AI modelling, and understanding of evolutionary or conservation genetics / genomics is desirable. Good teamwork
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observations and modelling of the physics and biogeochemistry of Antarctic shelf seas. You will gain experience in computer coding, statistics for environmental science, working with and piloting autonomous
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through The Lupus Trust. Training programme: Evidence synthesis, qualitative methods and analysis, mixed methods, statistical analysis potentially including meta-analysis, intensive longitudinal methods