26 analog-integrated-circuit PhD positions at Technical University of Denmark in Denmark
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processing power of a novel photonic integrated circuit architecture [Heuck2023]. This includes studying the effects of optical loss and decoherence and methods to overcome these by error detection and
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less understood. A significant failure mode is electrochemical migration, which leads to short circuits and fire risk. The morphology and chemistry of dendrites are determined by the humidity and gas
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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electrolysis Design of electronic circuits and power electronics Materials characterization Electrochemistry Moreover, the successful candidate is innovative and able to work in cross-disciplinary teams has good
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partners from Denmark and the Netherlands. Thus, these 2 PhD projects will be an integral part of a broader research team, consisting of collaborating partners with a common passion for autonomous
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data, which often only provides snapshots in time, neglect coastal waters, and overlook certain species. You will work on integrating diverse data sources to overcome these limitations and develop a more
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testing dynamic equivalencing methods for power system dynamic simulations and integrating these into commercial simulation tools. Dynamic equivalents are simplified representations of complex power system
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. The SOLARSPOON project will integrate living photosynthetic bacteria and food-producing bacteria into stand-alone photoelectrochemical devices tailored for the direct production of proteins and lipids from
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, hysteresis, oscillatory behaviour and support dependencies. Compare cluster behaviour across different characterization techniques (TEM, STM, TPD). Integrate findings to map the relationship between cluster
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bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models