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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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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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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
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the MET office, the candidate will use outputs from the JULES land surface model to run model experiments that test NBS adaptation potential under different climate change scenarios. For example
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case is central to this position. SimMobility is based on activity-based mobility modelling theory, simulating agent-level behavior such as route, departure-time, and mode choice within an activity-based
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We are looking for a postdoctoral researcher to develop and implement tools for analysis of output from Bayesian inference under phylogenetic models About the position A postdoctoral researcher
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the world are tackling global issues and making a difference to people's lives. We believe that inspiring our people to do outstanding things at Durham enables Durham people to do outstanding things in