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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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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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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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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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to discover new therapies. The successful candidate will assume a leadership position within the lab as a Postdoctoral Associate. Set the tone for both scientific and scholarly excellence. Drive forward complex
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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
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for comprehensive systems biology modeling. Identification of causal relationships and biomarker discovery through integrative approaches. Time-series and longitudinal multi-omics data analysis for disease