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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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reagents, development and optimization of novel organic reactions, as well as substrate scope investigation including isolation and characterization of products. You must have a two-year master's degree (120
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optimization of SERS substrates, Raman measurement, data analysis, and validation of results with reference methods such as high performance liquid chromatography (HPLC). You are expected to have a solid
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or passive components into organic substrates; has experiences in magnetic components design, optimization and integration; is familiar with the simulation tools such as Ansys (Maxwell, Q3D, Icepak), LTSpice
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the waste material from coating production and coating use. Test methods Reliable and fast test methods to optimize coating performance is of utmost importance in coatings development. We work on new test methods and
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and estimate tidal components. When relevant, compare with tide gauge data. Develop robust station infrastructure capable of withstanding extreme Arctic weather conditions. Optimize power supply
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to survive transients without exceeding load limits; ▫ maximizing cut-out wind speed(s); Key aspects include observing & inferring usable flow field quantities via the AWES device; trajectory optimization
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footage. You will process and annotate large-scale EM datasets, collaborating with fisheries scientists to ensure accurate species identification. A key task will be to optimize and validate algorithms