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to develop, optimize and apply advanced mass spectrometry approaches for the structural and molecular characterization of ARDCs, and to analyze and interpret the resulting data. - Develop and optimize mass
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optimization models. Investigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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optimization, evolving error correction techniques, and applications. We use locally hosted quantum computers based on superconducting technology in our experiments as well as the resources of our national
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and highly interdisciplinary research environment, bringing together expertise in theory, nanoparticle science, OLED technology, and organic chemistry. You will work closely with two ongoing PhD
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intelligence-driven approaches to industry challenges, such as warehouse management, ocean freight optimization, and reshoring high-technology industry sectors such as semiconductors and electric vehicles
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: - A PhD degree in optimization or systems and control theory. - A strong mathematical background and an interest in conducting theoretical research. - A proven track record. - Excellent English
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simulate a refined batch of synthetic data. For each batch, the postdoc will estimate Bayes optimal error, an important guide for realistic goals for deep learning. The first batch of synthetic data will be
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postdoc, you will contribute to research and teaching within advanced modelling, optimization and design of composite and lightweight structures for applications such as wind turbine blades. Your work tasks
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experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and components to automate