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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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by The Kempe Foundations. Project description Machine learning and artificial intelligence have had a major impact on medical image analysis in recent years. While CT and MRI provide highly
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. • Contribute to interdisciplinary research projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep
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methodology. Applying AI and machine learning (ML) tools (including Python, R, and possibly other languages) to test and evaluate biomedical hypotheses. Developing benchmarks and working together with staff
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 4 hours ago
to advance use of foundation models for Earth Science research and applications, including fine-tuning experiments Use of remote sensing, models, and/or machine learning to further our understanding
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome