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: Development of ML-based tools for analysis and engineering protein dynamics PhD enrolment: Czech Technical University in Prague DC14: Machine learning for Empirical Valence Bond (EVB) simulations to engineer
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 10 hours ago
interdisciplinary research environment at the interface of computational mechanics, mechanical/civil engineering, and scientific machine learning. The PhD candidate will also have opportunities to present their work
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 10 hours ago
mechanics, finite element modeling, and scientific machine learning. The RSE will contribute to the design, implementation, and maintenance of open-source software libraries that integrate phenomenological
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | about 10 hours ago
), is offering a fully funded PhD position in the area of statistical learning, machine learning, and survival analysis applied to large-scale proteomics and multi-omics cohort data. The PhD project
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team players and passionate on cutting edge computer vision and machine learning technologies, as well as possess deep understanding of machine learning technology and experience on turning machine
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systems, and space applications. We combine theory, physics-based simulations, machine learning, and autonomous workflows to understand and design materials that can perform under conditions where
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We seek a highly motivated Research Fellow to reverse engineer microstructured composite materials with novel