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independent research aligned with the aims of the Addiction & Decision Neuroscience Lab (ADN). Current work focuses on cognitive modeling of decision-making in both laboratory tasks and real-world settings, as
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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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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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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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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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-generation circular steelmaking. Using advanced atomistic modelling techniques, you will unravel the atomic-scale competition between copper and silicon at grain boundaries and oxide interfaces, delivering
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About the Opportunity Summary: Research involves developing and implementing material models to predict microstructure, phase change and residual stress in processes in high energy processes in
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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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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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, transcriptomics, metabolomics, phenomics, microbiome) for predictive modeling and biological interpretation. •Proficiency in Python, R, AI/ML frameworks, and bioinformatics pipelines for high-throughput data