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Field
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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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eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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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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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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Position Details Position Information Recruitment/Posting Title Postdoctoral Associate Job Category Staff & Executive - Research (Laboratory/Non-Laboratory) Department CINJ - Guo Laboratory Overview
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biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu/silverlab/ . Conduct independent research activities under the guidance of a faculty mentor in
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, Morocco, is seeking for a postdoctoral candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields. The candidate is expected
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are expected to have a Ph.D. in theoretical particle physics or related areas prior to the time of employment. Preferences will be given to those with experiences in collider phenomenology, machine learning