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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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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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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast
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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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on stability. Testing the model in standard stirred tank apparatus Refining the model to allow predictability between different types of apparatus. Defining an algorithm for testing enzyme stability
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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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communication skills are also required. Desirable attributes include experience with PCM systems, bioenergy, IoT-based control, model predictive control, digital-twin development, prototype commissioning