160 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Forschungszentrum Jülich
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Infrastructure? No Offer Description Work group: IAS-8 - Datenanalyik und Maschinenlernen Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student in machine learning to work within
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at the interface of computational systems biology and mathematics/statistics with a strong attitude to open research software development. For more information visit http://www.fz-juelich.de/ibg/ibg-1/modsim
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colleagues and for sporting activities alongside work Flexible working hours and appropriate remuneration In addition to exciting tasks and a collegial working environment, we offer you much more: https
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four different AC-voltages, independent of the distance covered https://arxiv.org/abs/2108.00879 . Theoretical consideration indicate good prospects for maintaining spin-coherence of the shuttled
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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, intermediate products, and finished products based on, for example, historical trade data ( https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37 ) Analysis of historical developments in material
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with our Welcome Days and Welcome Guide: https://go.fzj.de/welcome FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time ), allow you to
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remuneration for your thesis In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people with diverse backgrounds
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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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