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Mathjobs.org | 29 days ago
discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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<span lang="en"> Helmholtz-Zentrum Hereon</span> | Geesthacht, Schleswig-Holstein | Germany | about 20 hours ago
characterization.
The objective of the project is to bridge the gap between purely data-driven materials modeling and mechanistic microstructure simulations by combining crystal plasticity, dynamic
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at ETH Zurich, we aim to understand how materials deform, degrade, break, and ultimately fail. Our research is driven by curiosity about the physical mechanisms that underlie failure and by the
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 23 hours ago
based on deep learning to accelerate the physics simulation for membrane relevant processes, which can be based on physics-informed neural network, data-driven models, and hybrid simulation models
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ETH Zurich, we aim to understand how materials deform, degrade, break, and ultimately fail. Our research is driven by curiosity about the physical mechanisms that underlie failure and by the ambition
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. Ransell D'Souza at the Department of Chemistry and Materials Science, Aalto University, and the Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro at the Department of Chemistry and
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development of redox-active materials for electrochemical CO 2 capture and conversion. Computational modeling and data-driven analysis will be used to guide material selection, molecular design, and
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systems across multiple buildings Integration of data-driven and physics-informed learning algorithms for scalable energy management and control Engagement with industry stakeholders to guide practical
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
of model parameters from experimental and operational data under uncertainty using these metamodels Development of data-driven approaches for modeling residual prediction uncertainties and model-form