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Field
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Latin intellectual traditions. The project combines intellectual history with computational text analysis to examine large, historically complex corpora spanning multiple centuries and languages. The core
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. The main research themes are Artificial Intelligence, Computational Science, and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative
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processing, network resource management to improve the performance of the future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks
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architectures to solve data-driven sensing and control problems related to turbulent atmospheric flows. The work will center around investigation of reinforcement learning and convolutional neural networks
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regulatory proteomics and PTM biology and aims to generate mechanistic insights into how complex PTM networks regulate cellular function. MBG provides a highly collaborative and interdisciplinary environment
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of large-scale infrastructure and organisational complexity. This research stream goes beyond solving isolated engineering problems. It seeks to establish a new generation of AI-powered frameworks
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, geolocation and AI-enabled communication and networking systems. In these roles you will undertake research centered on the intersection of networking, communications, geospatial methods and machine learning
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or adaptive systems reinforcement learning, multi-agent systems, network or graph-based models simulation of complex socio-technical or organisational systems causal inference, econometric analysis, or formal
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate