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French National Research Institute for Agriculture, Food, and the Environment (INRAE) | Montpellier, Languedoc Roussillon | France | 2 months ago
with skills in statistics and modelling through machine learning (Agronomy joint research unit, MIA joint research unit, Wageningen University & Research); (ii) national and international laboratories
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spoken and written is required. The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/ ) at Institut Pasteur, led by Laura
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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ICT Services & Applications. Your role This position sits at the interface of machine learning, uncertainty quantification and computational biomechanics. You will work within the Legato group and in
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Research Framework Programme? Horizon Europe - ERC Is the Job related to staff position within a Research Infrastructure? No Offer Description The Machine Learning for Integrative Genomics team (https
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validate adaptive mechanisms for LoRaWAN based on machine learning techniques, targeting improved reliability and energy efficiency in mobile scenarios. To achieve this, it is necessary to go beyond
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Your Privacy When you visit any website, it may store or retrieve information on your browser, mostly in the form of cookies. This information might be about you, your preferences or your device and is mostly used to make the site work as you expect it to. The information does not usually...
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technologies, such as low-power long-range (LoRa) and high-throughput, low-latency technologies (5G). In the context of machine learning, communications play a central role in data sharing and in the decision
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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. 132, no. 3, pp. 1521–1534, 2012. [6] S. Koyama, J. G. C. Ribeiro, T. Nakamura, N. Ueno, and M. Pezzoli, “Physics-informed machine learning for sound field estimation: Fundamentals, state of the art, and