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The expert will participate in the necessary methodological developments and analyses of airborne data recorded by the IAGOS research infrastructure (https://www.iagos.org ) and from other networks, to provide
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | 3 months ago
devices will be able to perform computation and communication with energy harvested from the environment and from ambient wireless signals. The PhD student will be part of the design and electronic
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partnerships Represent the organization in international Frugal AI networks Build and supervise a research team, while mentoring junior researchers for skill development and scientific growth. YOUR PROFILE VITAL
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your publication record in leading international journals through collaborative publications ● Opportunities for participation in international conferences and building your network in the research
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• Participation in the international Long-Term Ecological Research network or a similar ecological research network • Experience working with spore-forming Firmicutes (bacilli) and/or Actinomycetes (culture
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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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Network “DyeAnotherWay” is inviting applications for a 36-month full time fixed term position as a Doctoral Candidate (DC). This research project is a collaboration between 17 organisations, including 9
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different methods of analysis used in the community, in particular linguistic probes (classifiers trained to predict certain linguistic properties from representations discovered by neural networks