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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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efficiency and reliability. Strong background in data analytics, leveraging insights to drive operational improvements and predictive maintenance. Experience in control strategies and automation, ensuring
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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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/environmental database and model-predictive control for indoor farming applications. Assist project team in deployment of testbeds and instrumentation for testing and validation of agritech/green building
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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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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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significant computational resources for centralized processing. Second, the existing centralized, terrestrial-based control infrastructure cannot scale with the increasing number of airborne sensors due
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. Oversees the delivery and operating model for UFA’s data management and reporting function–including roadmaps, Jira/ServiceNow workflows, release/change management–ensuring predictable, transparent, and
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two main directions: i) process-based studies and modelling - from local scale water balance studies to continental water resource assessments, and ii) statistically based regional and large-scale