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Field
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software engineering practices. Desirable Skills: knowledge of computer vision, Bayesian theory, or signal processing; experience with prototyping, experimentation, or technical evaluation of interactive
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data using descriptive statistics, visualization, mapping, and advanced statistical methods including Bayesian hierarchical regression, advanced difference-in-difference techniques for causal inference
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: Physics-informed neural networks (PINNs) & surrogate modelling Time-series modelling & anomaly detection Bayesian methods & uncertainty quantification LLMs & multi-modal models Graph neural networks (GNNs
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some of today’s most pressing societal challenges, including biodiversity loss, climate change, and environmental pollution. Our research combines advanced spatio-temporal modelling, Bayesian inference
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, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015). 3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and
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analysis and fitting hierarchical Bayesian models using ecological modelling software (Nimble, JAGS, Stan) will be considered an advantage. We offer you The well-being of our people and a positive approach
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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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statistics have been a key element in your work. Experience with data handling and flexibility in using a wide range of statistical methodologies, both frequentist and Bayesian. Demonstrated proficiency in