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Field
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. Development of real-time optimization algorithms and model predictive control (MPC) strategies for adaptive process management. Addressing data sparsity and data quality issues in industrial process data
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of cryptography is required. Expertise in optimization or efficient algorithm design will be considered an asset. Applications should include a CV, a list of publications and a research statement. Applicants should
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with the application of Algorithms, Artificial intelligence (AI), and Machine Learning (ML) methods to accelerate material discovery and design, optimizing manufacturing processes in nanoscience
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity
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-based tiles can be arranged and actuated to form tunable metapixels, enabling dynamic control of light at the nanoscale. This project will integrate algorithmic self-assembly and nanomechanical switching
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning for improving
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-exploiting optimization algorithms will be used to improve the performance of the numerical methods also for this class of problems. As postdoc, you will principally carry out research. A certain amount of
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control and energy-management algorithms for efficient and reliable long-duration operation. Implement hardware-in-the-loop (HIL) and real-time co-simulation for validation under realistic grid scenarios
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performance. 3) Detector systems: Characterization and modeling of HxRG detector readout schemes and analysis protocols. 4) Data analysis: Experience with instrumentation data analysis software and algorithms
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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning