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candidates must thus have a strong interest in algorithmic development as well as embedded hardware integration. Role and responsibilities This PhD project will be executed in close cooperation with
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develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms) for quantum networks with resource constraints. The project is funded by the EPSRC AI hub
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numerical modeling and validation of brain-inspired algorithms Develop circuit-plausible training and inference algorithms, and analyze their behavior in LTspice and Cadence Spectre Perform algorithm–circuit
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project focuses on the development of advanced models and algorithms
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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for application to fusion reactors, considering factors such as muon flux, detector sensitivity, and data analysis algorithms. PhD candidates will be tasked with developing simulations of complete detectors and
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on the project of NEMO Sub-Project C: Passive UE activity inference (4/5G) from OTA network traffic. The role will focus on developing algorithms and methods for processing and analyzing 5G signals. We
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design and reinforcement learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools
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the activities of GT3. The initial phases will focus on studying the ideal frameworks for creating the IT platform and developing AI algorithms for data analysis. In particular, the data storage structure will be
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems