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, segmentation, anomaly detection and learning in the context of non-stationary distributions. The candidate will also be connected to the Faculty of Information Technology. The Temporal Analytics Lab, directed by
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edge devices in distributed and collaborative IoT scenarios; ii) strategies for efficient and adaptive learning on-device or across a network of heterogeneous nodes while minimizing energy consumption
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the student to progressively acquire knowledge in scientific methodology, experimental research, data analysis, and scientific communication. As part of your doctoral studies, your duties will include
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collaboration. The successful candidate will work alongside an active research group specialising in AI, blockchain, federated learning, trust frameworks, and intelligent systems. This fully funded studentship in
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
) Collaborative Context and Expertise Since 2018, EDF has been studying the construction of meta-models based on deep neural networks (i.e., deep learning). Initially, based on fluid simulations using Code_Saturne
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Position Description The project will focus on the development and application of advanced data-analysis techniques for gravitational-wave science, including machine learning and deep learning
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Company Description By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and
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environment that includes international collaborations and research stays abroad. The PhD student is, in close dialogue with their main supervisor, expected to identify and refine their own research focus and