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and verifiably safe. We consider resilience and intelligence to be part of the same process. What you will do Machine learning techniques show promising results in visual quality inspection reducing
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computational methods using network-based analysis, machine learning and dynamic modeling. We are a young, dynamic team at the idyllic Dahlem campus and teach mainly in the Computer Science, Bioinformatics and
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Your Job: In this position, you will be an active part of our AI Consulting Team. Together with our partners, we develop new and innovative applications of Machine Learning. You will connect
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by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis
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and verifiably safe. We consider resilience and intelligence to be part of the same process. What you will do Machine learning techniques show promising results in visual quality inspection reducing
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, conservation genomics, museomics, metagenomics, annotation, machine learning, Instruct users in the usage of hardware and software for molecular biodiversity research, Acquire substantial third-party funding
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modeling (FEM), and thermodynamic/kinetic simulations using tools such as Thermo-Calc and CALPHAD first experience in applying artificial intelligence and machine learning techniques to Materials Science
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architecture exploration, hardware/software co-design and operating/runtime systems. Typical application domains are e.g. signal-/image processing, artificial intelligence and machine learning. Tasks: research
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learning for decentralized AI model training for tool wear detection and measurement in milling processes within the »FL4AI« project. A custom dataset has been acquired, consisting of microscopic tool wear
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Hybrid Crop Modelling Framework, integrating Process-Based Models (PBMs) with Machine Learning (ML) to enhance the accuracy and interpretability of crop yield forecasts, while evaluating key ecosystem