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Field
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understanding of molecular thermodynamics, and realize the importance of different types of properties in selecting and developing the most physically sound thermodynamic model for water and electrolytes
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challenges associated with capturing CO₂ directly from ambient air and is suited for candidates interested in combining computational modeling with experimental electrochemistry to develop energy-efficient e
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interpretable framework for tensor analysis. Specifically, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for
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: actual nerve biopsies from patients with Charcot-Marie-Tooth neuropathy due to YARS1 mutations, along with matching iPSC-derived motor neurons and Drosophila models. Now, we need a curious and driven PhD
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. This project focuses on fundamental algorithmic questions on geometric networks and, in particular, on geometric intersection graphs: graphs whose nodes correspond to disks or other objects in the plane and that
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Na+ transport, interfacial phenomena, and performance degradation induced by e.g. salt accumulation or Al deposition. By leveraging your experimental data with the modelling approach
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, interfacial phenomena, and performance degradation induced by e.g. salt accumulation or Al deposition. By leveraging your experimental data with the modelling approach of the collaborating Post Doc, we aim
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for our environment? Will the streets ever by quiet again? This PhD will give you the opportunity to build models that predict this! Job description In 2023, global drone shipments reached 1 million
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. 2) the development of computational models to enquire about the mechanisms that enable heterogeneous representations in neural networks. These models will be informed by experimental data. Duties
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outcomes Synthetic data generation (virtual patients) Statistical model checking to ensure statistical correctness of the results Machine learning–based classification and regression methods The candidate