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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring
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9 Jan 2026 Job Information Organisation/Company Academic Europe Research Field Engineering » Other Chemistry » Other Physics » Other Researcher Profile First Stage Researcher (R1) Positions PhD
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into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive experimental calibration. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth
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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
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relevant. The steps in the development of surrogate models are building data-driven models from medical imaging, extending them with physics-based approaches, and adapting existing physics-integrated neural
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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computational engineering, mathematics, computer science, physics, engineering or a related field Strong background in numerical methods and machine learning Proficiency in at least one programming language
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isotherm parameters directly from molecular properties. These predictions will be integrated into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive
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a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”. Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal
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, helping to shape future research infrastructure Present your results on conferences in Germany and abroad Your Profile: Excellent Master’s degree in statistics, physics, mathematics, or a related