91 composite-residual-stress-development PhD positions at Technical University of Denmark in Denmark
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will focus on developing a bioinspired, AI-driven BCI system that enables intuitive and adaptive control of a wearable wrist-hand exoskeleton for stroke rehabilitation. The system will integrate
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behaviour. This will include developing and using state-of-the-art image recognition algorithms to create digital twin models as well as statistical and machine learning methods for analysing large-scale
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qualifications The overall objective of this project is to upcycle excess earth from construction sites into sustainable structures. This will be achieved through the development of advanced, automated processes
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emphasizing the resilience of offshore marine infrastructures exposed to harsh and extreme environmental conditions. Through this PhD scholarship, you will contribute to the development of a physics-informed
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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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design to develop new high-potential organic flow battery electrolytes with unprecedented stability. The computational work will be done in close collaboration with an experimental counterpart
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qualified candidate to develop research and methodological tools to assess and to improve the welfare of farmed fish, contributing to the sustainability of aquaculture practices in the EU. We offer a 3-year
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with intelligent, data-driven emergency planning, aiming to develop robust tools for communities and authorities to prevent and mitigate the impact of natural disasters such as wildfires. Working closely
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no-show appointment rates. We develop methodologies for elderly care systems in rural areas by integrating primary and specialist care (coordinating between the sectors) using advanced data-driven planning
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industrial processes. Your research will drive a paradigm shift in how TES systems are modelled, integrated, and controlled within industrial settings. You will develop novel, adaptive, physics-informed models