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this, the CHAIN-H2 project will combine experimental and numerical studies covering small-scale kinetics through to modelling of the larger-scale characteristics of flame inhibition (flame propagation in a cloud of
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therapeutic education of patients and to support them in behavioural changes (nutrition, physical activity, etc.) related to their condition. The developed hybrid environment must notably: 1) Model and make
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of oxygenates from CO and/or CO2, and mechanistic studies including operando spectroscopy and possibly numerical simulations (microkinetics, DFT). The objective is to discover new eco-efficient catalytic phases
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interior and surface (imaging, observations, analyses and modelling in the fields of geophysics, geochemistry and digital science) to environmental and palaeoenvironmental themes, employing experimental
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. To test this hypothesis, the candidate will apply methods that include magnetoencephalography (MEG), brain stimulation, neurofeedback, and computational modelling. The project includes a collaboration with
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, combining theoretical (DFT modeling) and experimental approaches. - Study of reaction mechanisms: Identify key intermediates and reaction pathways that promote the formation of C–C bonds, using in situ
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prediction models, and visualizing immense volumes of various types of data, generated by agri-robots and IoT devices. The most popular classes of autonomous agricultural devices include: weeding robots
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/Particle Astrophysics Appl Deadline: 2026/04/26 04:59 AM UnitedKingdomTime (posted 2026/03/04 05:00 AM UnitedKingdomTime, listed until 2026/09/05 04:59 AM UnitedKingdomTime) Position Description: Apply
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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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. Argument(ation) mining, the new and rapidly growing area of Natural Language Processing (NLP) and computational models of argument, aims at the automatic recognition of argument structures in large resources