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Field
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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automated experimental systems to measure electrode performance and durability over extended operation. Investigate electrode material performance, aging, and degradation mechanisms for bioprocessing
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twins are leveraged to develop paradigm shifts in quality assurance, quality control, real-time process control, and design optimization for the United States. In this digital environment, you will use
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chemical–biological pathway. The advertised positions will support these efforts through research on advanced carbon capture methods, process modelling and optimization, and biological CO2 valorization in
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, including finite-element simulation and topological optimization of light guidance in HCFs, and numerical simulation of thermo- and fluid dynamics under fiber-drawing processes. Apart from the main tasks
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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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partners and optimize them for nonlinear quantum processes Collaborate with theory colleagues to refine fiber designs based on experimental feedback Disseminate results at international conferences and in
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areas: calculus of variations optimal transport theory theoretical aspects of machine learning Restrictions: During the period of employment, the employee cannot receive remuneration under another
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transformer-based architectures to create a powerful tool for understanding and predicting bacterial genomic sequences. The successful candidate will play a key role in developing and optimizing these models
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of 1,2,3-triazole derivatives, driven by femto- and nanosecond laser pulses. Special emphasis will be placed on the influence of chiral light in these processes. Our primary experimental objective is to