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Field
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Future. Discover. Together. The AI department at HHI is committed to advancing the field of artificial intelligence through innovative research and development. Our team focuses on creating cutting
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Artificial Intelligence – ScaDS.AI. We seek to fill the above position as soon as possible (e.g., Oktober 1, 2025). This interdisciplinary project investigates how physical systems can serve as computational
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automation in industry. Disruptive technologies such as artificial intelligence and quantum computing play a key role here. Fraunhofer IKS is conducting research to ensure that these applications are reliable
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, outstanding infrastructure and a high level of administrative support. The successful candidate will lead novel and innovative research in the broader field of Biological Artificial Intelligence. Areas
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on the analysis of observational and modelling data. This includes methods for analyzing different and in particular inhomogeneous data sets, also with the help of artificial intelligence (AI) methods, as
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
geometry, algorithmic complexity, and combinatorial optimization. Our contributions are regularly published in top venues like the Conference on Artificial Intelligence (AAAI), the Symposium on Algorithms
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough
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for complex geochemical systems, including deep sea hydrothermal vent environments of geothermal fluids. This project bridges modern artificial intelligence with geochemical modelling, aiming to deliver
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to real-time data acquisition during production, consulting, and prototype manufacturing. The employees conduct research in many innovative topics, such as artificial intelligence, data-based quality
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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