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investigate how AI methods and workflows can be designed and adapted to handle large, heterogeneous EO tasks efficiently and reliably. You will develop and evaluate approaches that address the trade-offs
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which technology and research fields are advancing. Furthermore, you will also extend the indicator beyond novelty toward quality assessment and validate the reliability, robustness, and fairness of LLM
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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share the goal of developing stable and reliable perovskite-based tandem photovoltaics, while maintaining complementary scientific responsibilities. FZJ focus: automated and data-driven development
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methods for automatic novelty assessment of patents and scientific literature Map the state of the art of scientific fields Investigate and evaluate the approach along the dimensions of reliability
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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components. We focus on application-oriented methods that can operate in real time and are suitable for operating decentralized systems. We aim to enable the reliable, cost-efficient, resilient, and
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. The performance of the developed methods will be evaluated using real operating data. In addition, it will be investigated how reliability and safety conditions can be taken into account. This is particularly
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environment perception and your own state estimation, and then integrating it into a newly developed trajectory and behavior planner. The goal is to enable safe, reliable and highly dynamic vehicle actions in
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agile behavior of the autonomous vehicle. You will be responsible for the conception, implementation and evaluation of the new algorithms. In addition to your technical work, you will take responsibility