155 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at Forschungszentrum Jülich in Germany
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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
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projects SCIENTIFIC ENVIRONMENT: You can expect excellent scientific equipment, modern technologies, and qualified support from experienced colleagues. On top, you will receive individual training to learn
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, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following tasks: You will work together
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experience in scientific computing and software development; familiarity with C++ and Linux environments is an advantage Strong background in deep learning for image analysis / computer vision, ideally
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mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time-lapse data Proven programming expertise in
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the collected data in accordance with FAIR standards for collaborative use within the Decode project and for machine learning applications Presentation of results in team meetings and preparation of a
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neuroscience is essential Experience with modelling, analysis of complex dynamical systems, simulation, analysis of large-scale datasets with machine learning methods, and software development are beneficial
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sound understanding of data evaluation Prior experience with single-cell data analysis, network analysis, or machine learning are a plus Good organisational skills and ability to work both independently
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environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people of diverse backgrounds, in terms of age, gender, disability, sexual orientation/identity as