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Field
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of thermal effects on force generation, material properties, and geometric clearances. Validate numerical models against experimental results and data available in the scientific literature. Disseminate
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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uniqueness problems in geometric evolutions arising from fluid dynamics models. We will focus on: (i) long-term regularity and the existence of periodic orbits for a class of free-boundary Euler equations
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disturbances. Current control methods generally rely on simplified interaction models based on constant aerodynamic coefficients, quasi-static approximations, potential flow models, or experimentally identified
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assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological data analysis and topological machine learning; AI-assisted theorem proving
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structure-preserving methods for mathematical models of physical systems, including topics such as geometric numerical integration, finite-volume and finite-element methods, variational formulations
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. This includes, but is not limited to: machine learning algorithms, formal proof assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological
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of visual learning theory, the PhD student will develop methods for understandable latent representations and implicit neural models. For further details, see the ELLIIT project Learning Geometric
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surface modelling; understanding of engineering drawing and design documentation practices; knowledge of dimensional chains, tolerances, fits, and geometrical tolerancing; ability to read and analyse
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 20 hours ago
(e.g. geometric deep learning, generative models, representation learning) to decode molecular physiology, pathology, and therapeutic design? Are you passionate about high-throughput biology and scalable