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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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for advanced courses, international research visits, and networking across Sweden’s top universities. Information about the research group The Computer Vision Group at the division of Signal processing and
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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of healthcare by examining the impact of bottom-up communities and networks promoting change. Healthcare accounts for approximately 5% of the UKs total carbon emissions, and significant activity is underway
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of healthcare by examining the impact of bottom-up communities and networks promoting change. Healthcare accounts for approximately 5% of the UKs total carbon emissions, and significant activity is underway
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the challenges of dynamic sensor networks for sleep management. Through the joint supervision between multiple disciplines, the student will be offered a unique opportunity to develop a robust personal portfolio
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optimising performance across multiple timescales and spatial domains. Systematically resolving these challenges in renewable-dominated power networks stands as a critical cornerstone for enabling the roadmap
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the genetic factors influencing changes in brain structures, using brain imaging, computational and statistical methods of network science. Project Aim: The aim of the project is to uncover the complex
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the technical and social challenges of Advanced Air Mobility (AAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare