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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired, method-driven theory to application-driven research. Please find more
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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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Infrastructure? No Offer Description Area of research: PHD Thesis Job description: Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use
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Simulation – Data Analytics and Machine Learning (IAS-8) at Forschungszentrum Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired
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planning and execution architecture for information-driven experiment steering (closed-loop control) Work in an interdisciplinary team of engineers, computer scientists, and life scientists Present your work
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-computing hardware Work in an interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state
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, they are in the same period as the previous year. You can find the current dates here: Application documents The application is submitted in two steps: 1. submission of the application documents 2. submission
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, computer science and earth science/engineering, or a related field Proficiency in at least one programming language (Python, Matlab, R, C++, Julia, …) Good analytical skills with a sound understanding of data
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engineering, biotechnology, computational biophysics, bioinformatics, data science, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling