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at two levels: SAACD Component: This is a UAV made up of hardware and software sub-systems, capable of observing, predicting, deciding and reconfiguring itself to fulfil its mission (e.g. surveillance
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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reputation and publication record in consciousness science, psychedelic science, predictive-processing, computational modelling, and integrative theoretical work. You will have a proven track record of
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Computational modelling of two-dimensional graphene-based materials School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Natalia Martsinovich Application Deadline
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scientific computing, to name a few. Modern LC applications rely heavily on accurate and efficient mathematical modelling of confined LC systems. Typical questions are - can we theoretically predict physically
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collisions recorded by the ATLAS experiment at the LHC. The analysis will be carried out in different energy regimes, including those that are still largely unexplored, in order to test the predictions
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Centre de Mise en Forme des Matériaux (CEMEF) | Sophia Antipolis, Provence Alpes Cote d Azur | France | 12 days ago
Infrastructure? No Offer Description The aim of this PhD is to model the development of microstructures during welding processes on thick parts, in the context of nuclear equipment, for which deposits of several
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foundation models to run predictably and efficiently on embedded processors and accelerators. FIND is a research program funded by the Dutch government and industry that brings together 5 universities, 11
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular