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Field
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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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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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such as novel sensors and computers. Working conditions The working atmosphere at the institute is largely determined by young, enthusiastic, mostly foreign employees. Communication is informal and runs
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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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, 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
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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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error-correcting codes, establishing fundamental performance limits, and building practical decoding algorithms and architectures. Your research will sit at the interface between the classical and quantum
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databases, information retrieval, data mining, or algorithms is expected. Excellent written and oral English language skills are required. The appointment is to be made in accordance with NTNUs guidelines
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companies. The research will integrate techniques of numerical analysis and structure-preserving algorithms to generative modeling in AI. It will build upon the work done at IMF and SINTEF in this field. We
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the photonic integrated circuit (PIC) sub-system of TESERACT (Twin Earth SEnsoR Astrophotonic CubeSat), a 6U mission demonstrating chip-scale spectroscopy in space and de-risking astrophotonic