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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
generation of wireless communication (6G) to extend network coverage, supporting diverse data-intensive applications such as immersive extended reality and autonomous systems. However, aerial 6G networks will
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the MSCA Doctoral Network Endotrain (Grant No: 101227148) and coordinated by the University of Bergen , Norway. Supervisors: Professor Stafford Lightman , Dr Thomas Upton Location: University of Bristol
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networks. The Pertz Lab has developed powerful optogenetic tools and fluorescent biosensors that allow direct perturbation and measurement of these networks using light. Together with the Ginsbourger Group
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. The position is funded by the European Commission through the MSCA Doctoral Network Endotrain (Grant No: 101227148) and coordinated by the University of Bergen , Norway. Supervisors: Professor Stafford
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, an expert-driven, manual, and time-consuming process used to simplify CAD models so that meshing tools can cope with small-scale features such as fillets and manufacturing details. This de-featuring is not
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networks and in mobile & edge computing platforms. Where to apply Website https://networks.imdea.org/job/phd-position-on-safe-machine-learning-msca-final… Requirements Research FieldEngineering » Electrical
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costs will be deducted in accordance with the applicable national regulations. Please find additional information in the Information package for Marie Curie fellows in doctoral networks ( https