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microstructures along the entire process chain using machine‑learning (ML) techniques and validate soft‑sensor outputs against laboratory reference measurements Perform systematic laboratory flotation experiments
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using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts generated in the scattering
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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processes that produce energy and raw materials. The Department of Thermodynamics of Actinides is looking for a PhD Student (f/m/d) - Machine Learning for Modelling Complex Geochemical Systems. The job
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simulation environments, numerical methods, or machine learning approaches is an advantage Fluent command of written and spoken English is necessary; German is an advantage but not required High degree
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using X-ray and neutron scattering. The main research areas are materials for photovoltaics, proteins in solutions and at the interfaces, complex nano-structured materials and machine learning tools
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Particle Acceleration is looking for a PhD Student (f/m/d) Multimodal Reconstruction of Laser-Electron Accelerator Phase Space using Physics-Informed Deep Learning. Your tasks Understand the physical process
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knowledge of machine learning (e.g., in the areas of object detection and identification, generative AI, etc.) Good written and spoken English skills (min. level B2) Good written and spoken German skills (min
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to obtain further academic qualification (usually PhD). Tasks: scientific research and development activities in research data management (RDM) with a focus on AI- and machine learning-based methods