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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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7 Jul 2026 Job Information Organisation/Company UNIVERSIDAD POLITECNICA DE MADRID Department HRS4R Research Field Computer science » Other Researcher Profile Recognised Researcher (R2) Positions PhD
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Max Planck Institute for Intelligent Systems, Stuttgart, Stuttgart | Stuttgart, Baden W rttemberg | Germany | about 1 month ago
projects. The student will work closely with the group leader and PhD students to implement and maintain computational mechanics methods that serve as the foundation for future research activities. Typical
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in tropical regions; analyze links between macrofauna and soil carbon; build/validate scoring algorithms using machine learning/cumulative functions. Outputs – Lead scientific, technical, and policy
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for the propagation of incoherent light, novel computational algorithms for solving inverse problems to retrieve denoised and high-resolution holographic recordings, and motion compensation techniques. In this PhD
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, Algorithms, and Data" team at L2IT. Application procedure: Interested candidates must send: 1. A cover letter presenting their research project and their interest in joining the group 2. A Curriculum Vitae 3
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needed to improve our understanding, management, and exploitation of the electromagnetic environment through the application of AI technologies. This involves both the application of existing AI algorithms
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primary deliverable of this position is a theoretical and methodological template that we call Action-Oriented Modeling. Rather than delivering finished algorithms, the template specifies how to build
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modeling of novel radiopharmaceuticals, development of advanced image reconstruction algorithms for quantitative PET and PET/MR imaging and the development of deep learning methods to improve quantitative
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-guided design-build-test-learn workflows, and open algorithms, benchmarks and reproducible research. This is an exciting opportunity to help shape a frontier AI for Biology programme, publish high-impact