82 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" scholarships at Forschungszentrum Jülich
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): https://go.fzj.de/JuDocs SUCCESSFUL START: It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and
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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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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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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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): https://go.fzj.de/bmi.tvoed PERSPECTIVE: The position is initially for a fixed term of 3 years but with the prospect of longer-term employment SUPPORT FOR INTERNATIONAL EMPLOYEES: Our International
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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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and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs Targeted services
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data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ 30 days of annual leave (depending on agreed working time arrangements) and provision for days off between