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Sharing – Building a federated data space to enable responsible data integration and cross-project learning. AI & Modelling – Using shared data to power advanced models that help describe and predict
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prediction, focusing on efficient edge deployment (e.g., through model pruning, quantization, or TinyML techniques). The embedded system will be designed to perform local inference in real-time, minimizing
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Join us for an exciting Doctoral student journey that will combine systems biology, computational modeling, and industrial biotechnology to solve a key challenge in sustainable biomanufacturing
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materials to enhance the cell robustness. Work plan The work plan for the PhD thesis will be divided in three main steps: 1) A chemo-mechanical model will be built to predict the crack initiation and
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recover quickly from disruptions. The research will involve reinforcement learning, predictive modeling, and real-time adaptive control to dynamically optimize production sequencing, resource allocation
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sequencing, and predictive modelling to define the immediate molecular consequences of light and temperature signals. One crucial component of plants’ sensory network is the circadian clock. In plants
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projects on our Epic electronic health record platform. You will focus on integrating AI and predictive models into the Epic platform, enacting and facilitating standardized intake, design, and
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to reduce resource consumption and make SF State a model of sustainable best practices. Effectively manage projects and daily operations to ensure that new rules, regulations, or other changes in operations
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design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast user actions and remote system responses
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significant computational resources for centralized processing. Second, the existing centralized, terrestrial-based control infrastructure cannot scale with the increasing number of airborne sensors due