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Field
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the Research Group “Nonlinear Optimization and Inverse Problems” (Head: Prof. Dr. D. Hömberg) starting as soon as possible. The project is part of a BMBF project concerning industrial scale data preparation
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: · Developing mathematical optimization approach and tool for optimal operation of smart low voltage grids with high penetration of distributed energy resources · Producing quality research outputs (e.g
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critical quality attributes Optimize design and process parameters to ensure efficient production of highly potent mRNA Evaluate multiple DNA-based platforms for optimal mRNA potency and process parameters
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electromagnetic simulation techniques and incorporate the characteristics of new materials to design motor structures, and perform multi-objective optimization aimed at enhancing motor efficiency, power density
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based at the Department of Obstetrics and Gynaecology, University of Cambridge. They investigate the mechanisms by which sub-optimal nutrition in early life can affect reproductive ageing, the impact of
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. Building on this work, we seek to harness the power of this new tool to optimize its therapeutic use for a wide variety of conditions including epilepsy, neuropsychiatric disease, movement disorders, chronic
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based at the Department of Obstetrics and Gynaecology, University of Cambridge. They investigate the mechanisms by which sub-optimal nutrition in early life can affect reproductive ageing, the impact of
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leveraging real-time big datasets from distributed energy resources (DERs). · Contribute to integrate optimal solutions for operating local energy markets into Real-Time Hardware-in-Loop (RT-HIL) lab
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aspects of the research project(s), including design, fabrication, characterization, and modeling of metamaterial fibers and textiles. Develop and optimize fabrication processes for fibers and textiles
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respective fields. Through AI-driven insights and data analytics, we provide personalized support, including identifying suitable programs, optimizing application materials, and preparing candidates