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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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optimization of GRAS microorganisms at 1-10 L fermentation scale. ii. Design and optimise downstream bioprocessing for protein recovery. iii. Perform characterization of protein isolates for incorporation
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preparation protocol optimized for low-quality DNA extracted from fuel samples. The initial appointment is for three months, with potential for extension based on funding availability. Key Responsibilities
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chip. A strong background in the design and fabrication of silicon photonic devices, advanced characterization techniques, and an interest in semiconductor materials optimization is highly desirable
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bottlenecks and optimization strategies. The digital twin will serve as a testbed for evaluating engineering trade-offs and guiding future hardware development. The appointed researcher will collaborate with
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the field of Artificial Intelligence (AI) and Data Optimization. As a Researcher in Gen-AI and Data Optimization, you will be actively involved in a cutting-edge research project focused on applying
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need optimally. Finally, in a multi-site study, we will test whether using this system and escalation pathways leads to better outcomes. We are seeking an enthusiastic, ambitious, clinical researcher
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through long-term impact assessment and optimization. The goal is to develop a framework to estimate carbon emissions across AI's development, operation, and use. This framework enables stakeholders
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breakwaters under varied sea conditions. 2. CFD and Finite Element Modeling Perform Computational Fluid Dynamics (CFD) simulations to optimize the design and performance of floating breakwaters. Develop finite