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data sets, which have to be evaluated in order to obtain a holistic understanding of very complex systems. Visit HDS-LEE at: https://www.hds-lee.de/ The position is placed at the Institute for Advanced
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: Design hierarchical models that explicitly capture misspecifications in metabolic models Develop differentiable and scalable inference algorithms using automatic differentiation Implement HPC-tailored
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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type, developmental stage, treatment) to build tissue- and context-specific co-regulation networks Design and implement clustering and integration approaches (e.g., network-based and subspace clustering
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-source software development and industrially relevant applications. Tasks include: Development of molecular descriptors from protein structures and simulations Design and training of QSPR and machine
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course of study in the fields of Fine Art, Design, Visual Communication and Film. The scholarships also promote the exchange of experience and networking amongst colleagues. Who can apply? You can apply if
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing
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modelling and the land-surface model used in the project. Develop simplified, fast-running model surrogates using machine-learning methods to replace very time-intensive simulations. Design an efficient