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Field
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collected by the ITk will be a major challenge due to the extremely high combinatorics involved in the high-luminosity conditions of the HL-LHC. Without significant improvements in reconstruction algorithms
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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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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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algorithms, and some knowledge of data science and machine learning (through coursework, self-learning, or personal projects). The selected student will work with Ph.D. and master's students to help develop
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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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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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to the IE faculty's Doctoral Programme (https://www.ntnu.edu/ie/research/phd/ ), see Section 6-1 of the PhD regulations for more information. You must have a relevant Master's degree in Computer Science and
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lead to better-behaved inverse problems. These mathematical insights will subsequently be translated into computational algorithms for inversion, uncertainty quantification and experimental design. The
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the work aligns with your goals and aspirations. For more details see https://binyshlab.com/positions/ What will you do? Conventional robotic bodies rely on computationally intensive centralized control and
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advanced algorithms for solving complex optimization problems. You will work with real-world case studies to design future-proof supply chain networks for key agricultural products in vulnerable regions