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description: The Scientific Computing Center is the Information Technology Center of KIT. The Research Group Exascale Algorithm Engineering of SCC works at the interface of algorithmics, parallel computing, and
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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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skills Confident working in dynamic environments with a focus on efficiency and prioritizing parallel projects What you can expect Fascinating challenges in a scientific and entrepreneurial setting
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are developing an automated disassembly system for electric vehicle batteries. A key component of this process is the rapid and reliable evaluation of individual cell health. This thesis contributes to this vision
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sintering press with selected copper pastes, followed by detailed characterization of the resulting interfaces in terms of porosity, thermal and mechanical integrity. In parallel, simulation models will be
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build reliable, reproducible data flows for large EO datasets and workflows Lead performance engineering (parallelization, optimization, benchmarking) for adaptation and inference at scale Work closely
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possible. What you will do You will be trained step by step in plant operation and actively support ongoing production processes. You will support the execution of quality controls in parallel with
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economy Logistics systems for specific sectors (e.g., retail, healthcare) You use methods from at least two of the areas of process management, mathematical optimization and/or simulation. You show
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architecture, parallel processing and hardware-oriented applications and optimisations as well as operating systems. The project will be carried out in close cooperation with Audi in Ingolstadt and Neckarsulm
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for large EO datasets and workflows Lead performance engineering (parallelization, optimization, benchmarking) for adaptation and inference at scale Work closely with industry partners and public agencies