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and at most 17 hours. Be part of change CAE modelling and numerical simulation (e.g. sheet metal forming processes using ABAQUS, etc) Develop data-driven and machine learning-based models to improve
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The »High-Performance Cutting « department develops technologies and application-oriented solutions for machining along the entire process chain - from process design and process simulation to real
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://www.microman4health.eu/ ). The described open position has the topic “EDM Machining-State Analysis from Discharge Signal Data” and is supervised by Prof. Dr. Andreas Schubert. This network focuses on developing data
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failure modes of electrical machines and their effects on vibration behavior are simulated. To this end, you will first adapt existing finite element models (FE) to represent mechanical faults. You will
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of laser technology—is offering the opportunity to write a thesis on the topic: »AI-based local quality prediction for laser powder bed fusion«. We are currently developing machine learning-based approaches
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simulations for glass molding processes, including model setup, meshing, debugging, result extraction, and evaluation. You will also post-process simulation data in Python for optimization and machine-learning
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methods shall make new technically feasible and requirement-compliant engine concepts quickly accessible. Your main tasks include: AI-supported component selection: investigation and preparation of existing
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elements. By integrating machine learning, structural optimisation, and automated code-compliance verification, the platform will enable designers to create structural designs based on available reclaimed
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or more of the following areas is desirable: - Thin Film Electronics (TFTs, Organic, Hybrid, Low-Dimensional/2D Materials) - Bio-Electronics (Neural Interfaces, Optrodes) - Sensors & Human-Machine
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limited time until 31st of December 2032, available immediately Key Responsibilities: Design, implement and refine automated image analysis workflows, including machine learning–based methods