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exergetic analysis, including control strategy. The net monthly salary is €2,200 The contract duration is 3 years Where to apply E-mail Daniel.matt@umontpellier.fr Requirements Research
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provided by the EUROfusion Horizon Europe consortium, which offers access to a large collaborative network and European EURATOM funding. To model the behavior of hydrogen in materials, we use electronic
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, workshops, and networking events. Application Procedure Applications should include: A cover letter describing motivation and fit for the project. A CV with details of education, research experience, and
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optimization of complex systems, intelligent data and information systems, as well as networks, distributed systems, and security. LIMOS stands out for its interdisciplinary approach, combining theoretical
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(from clinical or environmental use cases) Application of improved workflows in field studies These tasks will be carried out within the Marie Skłodowska-Curie Doctoral network METAMIC 3 - Metaproteome
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and adapted tools for the processing of signals or images acquired with biomedical sensor networks (cardiology, neurosciences) or in geosciences (seismology and marine ecology), but also in wireless
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zero net magnetization. The objective is to understand the interplay between magnetic and structural degrees of freedom gives that give rise to this novel phase, and to explore how the properties can be
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Networks in order to handle non-linear relationships between covariates and response variables. To this aim, the PhD student will join a consortium of researchers issued from different disciplines with a
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Arts et Métiers Institute of Technology (ENSAM) | Paris 15, le de France | France | about 1 month ago
. This issue can have safety implications, particularly in closed-loop setups. Physically Informed Machine Learning (PIML), and in particular Physics-Informed Neural Networks (PINN), are less dependent on data
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approaches that build processes by mutation operators [1], natural language processing techniques with recurrent short-term memory (LSTM) neural networks [2], and variational autoencoders (VAE