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point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data
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. Integrate physical laws, experimental data, and simulation results into unified machine learning frameworks to improve model robustness and generalizability. Conduct data preprocessing, model training, and
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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: Design, implementation, validation, and evaluation of Artificial Intelligence and Machine Learning models for UAS identification and classification based on AI and RF spectrum occupancy data sets
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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participate in the development and implementation of Machine Learning algorithms for pattern recognition and automated simulation scenario generation. They will also contribute to the technical evaluation
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DEVCOM Analysis Center. Topics of particular interest include: 1. Development of novel machine learning and AI models, as well as the adaptation of existing approaches for AI-enabled decision aid systems
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experience working with frontend/backend web development, machine learning, or high performance computing would be desirable, as would any experience designing or delivering training courses. This post is
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learning environment. Your main tasks will be: Establishing models of B and T cell priming in human lymphoid organoids. Performing characterization of organoids and primary samples with flow cytometry and