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Field
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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-on experience in environmental management. Interdisciplinary training in hydrogeology, contaminant transport modeling, and decision-making. Networking & knowledge exchange with leading academic and industry
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such as SHACL (Shapes Constraint Language), the NCT3TA reference model, and standards like HISO 100xx and ISO/IEC 11179-xx. Prior experience with these tools is not required—training will be provided
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of the Hub. Our approach enhances T2 (Interconnected QC systems) through verification methods for connected networks, supports T1 (Integrated quantum demonstrators) via hardware-agnostic metrics, and enables
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of written and spoken English (C1 Level), Collaborative, team-minded and pro-active attitude. High level of written and oral communication skills. Experience with the structures of the University of Vienna
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consider diagnostic waste. The post-holder will be required to demonstrate knowledge of sustainability issues impacting the medical devices industry. They will be expected to work as part of a wider research
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Join our diverse and inclusive team to transform the future of aviation as part of the UK’s EPSRC Centre for Doctoral Training in Net Zero Aviation. Offering. Offering fully funded
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the paper industry. Motivation This project will form part of a wider network of European partners spread across Europe from Universities (Nottingham/Ghent/Maastricht), smaller SMEs (Nova Biochem), Research
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Programme: Hybrid CFD and process simulation for process intensification of post-combustion CO2 capture School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof