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Field
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and
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interdisciplinary research It would be considered beneficial if the candidate has knowledge of one or more of the following topics: XAI, NLP, law, latent space analysis, uncertainty estimation. Qualification
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for clinical use. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning
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(e.g., in the context of risk, uncertainty, future thinking), social processes (e.g., interpersonal dynamics, social norms, and influence), and behavioral outcomes (e.g., behavior change, social action
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techniques like generalisations of Autoregressive Integrated Moving Average (ARIMA) models, Dynamic Linear Models (DLM) and joint longitudinal and survival models. To appropriately capture uncertainty
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sector-coupled energy system will face major challenges with handling the uncertainties from variable renewable energy (VRE) sources like wind and solar power. The power system operators will need
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from multiple sources to estimate air quality, along with associated measures of uncertainty. Some traditional models can be relatively restrictive in nature and lack capabilities to deal with large
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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cognition and emotion in judgement and decision-making (e.g., in the context of risk, uncertainty, future thinking), social processes (e.g., interpersonal dynamics, social norms, and influence), and
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in judgement and decision-making (e.g., in the context of risk, uncertainty, future thinking), social processes (e.g., interpersonal dynamics, social norms, and influence), and behavioral outcomes (e.g