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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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evaluation Knowledge of numerical simulation, for example with land surface or hydrological models Genuine interest in data science and earth sciences Good organizational skills and ability to work both
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will focus on simulation, experimental characterization, and analysis related to high-speed electronic devices. Key Responsibilities: The research assistant will focus on the following tasks Numerical
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, molecular simulations, and their application to bioprocesses and bioseparations Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling
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assemblies. Operate solar simulators, thermal and electronic sensors, and electrical measurement equipment. Assemble prototype TEG–PCM roof tiles and perform durability, thermal cycling, and performance
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one programming language (Python, Matlab, R, C++, Julia, …) Good analytical skills with a sound understanding of data evaluation Knowledge of numerical simulation, for example with land surface
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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thermodynamic simulations. The research findings of WERA can be transferred to many other fields of the circular economy. PhD study in WERA takes 4 years in a close cooperation between PhD students and
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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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focus and its numerous resources (e.g., databases, research seminars, brown bags, career planning, skills-enhancement workshops, international conferences, contacts with practitioners, etc