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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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to humans and are accessible to algorithmic techniques while neural models are adaptive and learnable. The aim of this project is to develop models which combine these advantages. The project includes both
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. Develop new machine learning methodologies (from artificial neural networks, decision trees, evolutionary algorithms and others) compatible with epidemiology. Produce a digital twin for national suicide and
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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doctoral programme coordinated by the UAB, designed to recruit and train 25 doctoral candidates. The programme focuses on advanced materials, innovative methodologies, and transformative solutions
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development Signal processing and control algorithm implementation Communication protocol design and optimization Cross-disciplinary teamwork Specific Requirements Collaboration with multidisciplinary teams
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
distributed optimization with noisy communication channels, accurately study the selected algorithms, participate in the development and maintenance of the PEPit (https://pepit.readthedocs.io/ ) software
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while developing transferable skills in an international, interdisciplinary setting. The 15 open positions: DC1: Low-power IoT sensing at high frequency bands Host: IMDEA Networks Institute, Madrid, Spain
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, (project number PN-IV-P7-7.1-PED-2024-1573, contract no. 68PED/2025). NavigAIte is a research and development project focused on creating an on-board localization system for unmanned aerial vehicles (UAVs
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy