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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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translate fundamental insights in membrane technology into practical, impactful solutions. Responsibilities and qualifications Develop and optimize highly structured membranes for membrane distillation. Lead
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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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mathematical and analytical models to predict coil loss, facilitating the optimal design of HPMCs Constructing a large-signal platform to measure coil loss of HPMCs Exploring innovative solutions, such as new
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for Surface enhanced Raman Scattering (SERS). You will spearhead and coordinate our development and optimization of new SERS substrates as well as sample preparation of clinical samples that we receive from our
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for the position. Knowledge of TSN or real-time networking. Experience with one or more of: combinatorial optimization algorithms, ML algorithms, timing analysis network calculus, real-time systems. Excellent
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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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CO2 capture from the atmosphere. Your objectives will include to: Develop new optimization and/or machine-learning based reconstruction and segmentation algorithms to improve image quality in time