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understanding of non-stationary complex systems through theoretical analysis and numerical simulation develop efficient statistical algorithms for analyzing and inferring dynamical models from multivariate time
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discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease progression modeling. Explainability and interpretability of AI models to support clinical decision
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 20-Aug-25 Location: Houston, TX, United States, Type: Full-time Internal Number: 5029 Special Instructions
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Postdoc Impact of Computational Infrastructures on Public Institutions and Administration of Justice
study how computational infrastructures develop over time through shifts in software production. We do so to critically engage with these transformations and explore more equitable ways of organizing
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A postdoctoral position is available for highly motivated individuals with a background in Computational Neuroscience to analyze complex time-series data from ongoing 2-photon microscopy experiments
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction
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complements LCAB’s research programmes. High competence in the analysis of large ecological and other data sets and time series. Ability to write up research for publication in high profile journals, along with
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in time series, tensor data analysis, and related topics. Duties include identifying important problems and novel approaches related to analysis of tensor and other complex time series and related
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models, attention mechanisms, and foundation models. 3) Time-Series Analysis : Specializing in modeling, forecasting, and anomaly detection to address complex real-world challenges. The successful
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
datasets. Developing explainable AI (XAI) models to facilitate clinical decision support systems (CDSS) and enhance trustworthiness in healthcare settings. Addressing longitudinal and time-series data