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Field
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field. Documented experience with soundscape-related research methods, such as soundscape prediction, soundscape design or soundscape evaluation; psychoacoustic or perceptual modelling; auralization
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is expected to contribute to foundational research in dynamic modeling, stability analysis, and control of coupled data center–grid systems. Emphasis will be placed on developing new theoretical
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on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data. The successful candidate will work on projects involving the analysis
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computing, cloud-based computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence
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model disease trajectories to identify risk factors and improve disease prediction and prevention. Responsibilities will include conducting detailed analysis of multi-modal data from the UK Biobank, in
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, transcriptomics, metabolomics, phenomics, microbiome) for predictive modeling and biological interpretation. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data
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role of land–atmosphere interactions in S2S predictability; impacts on boundary layer processes, aerosol-cloud interactions, precipitation, and hydrological extremes, including feedback mechanisms
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role in developing and implementing novel computational models within a boundary element framework, coupling the BEM with models for flow separation, viscous drag and cavitation. There is room to shape
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accessibility), and clinical data. * Develop, apply, and benchmark machine learning and statistical models for subtype discovery, classification, and outcome prediction. * Contribute to the development
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not limited to) identifying effective tutoring practices, running experiments on AI tutors in simulated and real-world environments, fine-tuning AI models to classify qualitative data, and building