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data with field-collected biodiversity and ecosystem functioning measurements to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response
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to understand how biodiversity and ecosystem functioning are linked and changing across different habitats in response to climate change and/or human use of natural resources. The successful candidate will
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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
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emerging areas, and currently covers the following topics: Signal and image processing theory Statistical signal processing, non-stationary processes, Bayesian inference, signal models, sampling theory
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development. Experience with implementing statistical learning or machine learning (e.g. Bayesian inference, deep-learning). Programming skills in Python and experience with frameworks like PyTorch, Keras, Pyro