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the case of challenging environmental conditions, the method of multi-scale parallel single-pixel imaging has the potential to enable breakthrough advances. The “image processing and AI” group contributes
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time is to be minimized by developing a robot ensemble for parallelized polishing as well as surface roughness and shape characterization. The »3D Sensors« group contributes to this goal by developing a
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compatibility that will be used to rapidly evaluate the generality of key reactions. Key reactions showing a promising generality profile will then be subjected to a reoptimization process. This is necessary
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classical calibration pipelines, including camera models, calibration targets, and algorithms, followed by an overview of our in-house specialized methods. In parallel, you will work in experimental setups
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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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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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program generation and optimization Verification, testing and security of software systems Explainability of AI and of software engineering Software for distributed, highly-parallel AI systems Intelligent
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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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focus areas in computer 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
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, we 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