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Field
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Ultrasound sensors for quantitatively measuring the viscosity of flows. Job description Continuous, non-invasive measurements of fluid properties are instrumental in factories that produce food
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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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of trustworthy neurosymbolic AI. There is both a theoretic and practical component to this topic, since the ultimate goal is to implement efficient algorithms that allow reasoning engines to produce
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predictive maintenance of ships and maritime systems. Modern vessels generate large amounts of heterogeneous operational data from sensors, machinery, control systems, maintenance records, and other sources
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global data such as commodity prices and weather predictions. Technological progress has made it possible to automatically collect a variety of sensor data and self-reported practice. The challenge is to
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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involving Prof. Dr. Michael Bader (TUM CIT, Hardware-aware algorithms for HPC) , Prof. Dr. Felix Dietrich (TUM CIT, Physics-enhanced Machine Learning) , and Prof. Dr. Hartwig Anzt (TUM CIT, Computational
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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of video and low-cost sensor technologies to capture subtle movement patterns, creating a rich dataset for AI-driven analysis. Machine learning, deep learning, computer vision and multimodal AI methods will
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, you will focus on developing mathematical models and numerical algorithms that systematically integrate uncertainties into the design process of optical systems. The goal is to enable novel design