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(PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: the project: i. Perform fermentation and optimization of GRAS
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candidate will work on modelling, analysis, and optimization of circular agri-food supply chains in the UK with a broader emphasis on identifying data-driven and practical solutions to minimize food waste and
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candidate will work on modelling, analysis, and optimization of circular agri-food supply chains in the UK with a broader emphasis on identifying data-driven and practical solutions to minimize food waste and
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to advanced AI, ML, xAI, and optimization for sustainable energy systems analysis and forecasting and being familiar with large language models, cloud platforms and distributed computing environments Experience
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(PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: Perform fermentation and optimization of GRAS microorganisms at 1
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that automate and optimize Amazon ad campaigns using AI and machine learning, including LLMs Apply knowledge in a way which develops processes suitable for application in industry. Integrate AI for Campaign
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and predictive analytics. AI-driven digital tools for material optimization and certification processes. Collaborative work across industrial and academic sectors. The Role The work involves leading
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-edge research and development in EIT-based tactile sensing, machine learning for real-time state estimation, and sensory-motor control optimization for robotic systems. Funding is available for this post
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://research.nottingham.edu.cn/en/persons/jie-yu About the Project. This research project aims to explore innovative strategies for green supply chain management and carbon footprint optimization in the context of dual carbon
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communication skills. Bonus qualifications: Experience with aerodynamics & aeroelastic low-/mid-/high-fidelity simulation tools for loads calculations; Experience with developing, training, and optimizing neural