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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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. Transition metal-based complexes, in particular, offer rich spin properties that can be tailored through ligand design. This project aims to explore and optimize such molecules to enhance their performance as
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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
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is hosted by the new Institute for Advanced Membrane Technology (IAMT). The PhD will be registered in the Faculty of Chemical and Process Engineering. Contact Prof. Dr.-Ing. Andrea I. Schäfer
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interdisciplinary projects. Development, testing, and evaluation of methods and software systems directly on our real test vehicles (see image) for automated driving. Application of modern software engineering
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quantification, model-order reduction, or multi-fidelity methods. The primary fields of application are life science, medicine and health, earth observation, and robotics. Consequently, a MUDS student will learn
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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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characterization using spectroscopic and microscopic methods is required. • Experience in membrane separation and vacuum apparatus is highly desirable. • As an international research institution, we require a
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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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cells. Your tasks Cultivation of mammalian cells Biochemical as well as cell and molecular biological methods to study the interactions of alpha emitters with mammalian cells Characterization