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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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. This project seeks to advance energy autonomy by optimising power conversion, storage, and distribution in such systems, enabling broader adoption in real-world applications. The project aims to develop a PMC
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focus on how interactions between species at different trophic levels shape these responses. The project combines (1) analysis of long-term datasets to quantify historical changes in the distributions
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a
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PhD Stipends within Distributed, Embedded and Intelligent Systems (DEIS) At the Technical Faculty of IT and Design, Department of Computer Science, one PhD stipend is available within
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at the receiver, steep terrain between the turbines and the receiver with occasional diffraction during propagation. A source model to explore is a distribution of static monopoles (non-aeroacoustic sources) and
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the transition to a low-carbon society within 2050. This transition is likely to influence society in many ways. The transition to zero-emission technologies would be different for passenger and freight
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and application architectures with implementations of massively distributed embedded systems that interact with each other and their environment to enable secure, goal-driven, autonomous and evolvable