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position is the development of novel machine learning methods for modeling molecular properties, in particular regression models for bi-molecular properties. The research is embedded in the thematic context
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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systems, including methods for colloid characterization Spatially resolved surface analysis using interference microscopy and autoradiography Derivation and parameterization of mechanisms Interdisciplinary
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looking for a PhD Student (f/m/d) to work on the Development and Evaluation of PET Image Reconstruction Methods for Simultaneous Clinical Dual-Tracer PET Imaging. Positron emission tomography (PET) is a
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laboratory is required Knowledge of handling radioactive materials and radiochemical methods is an advantage Excellent knowledge of a broad range of microbiological, chemical and analytical methods
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profile Completed university studies (Master/Diploma) in the field of Physics (Computational-, Plasma Physics, Optics) or related field Mastery and use of the scientific method Experience in numerical
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challenges facing modern societies. Specifically, the tasks are: Identify state‑of‑the‑art machine‑learning (ML) methods that can be applied to geochemical systems in geological contexts Assess these methods
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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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developing methods to transfer ion-induced damage results to neutron-induced damage scenarios and accelerating material development cycles. The findings provide not only a scientifically sound assessment of