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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg National Research Fund. MICRO-PATH, or Pathogenesis in the Age of the Microbiome (https
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This position is inside the SPETRA doctoral training unit which investigates new materials, methods and concepts for converting sunlight into usable energy sources. Inorganic Chalcogenide
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diverse backgrounds (e.g., economics, computer science, information systems, engineering, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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Applications should include: Curriculum Vitae Cover letter Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally through the HR system. Applications by Email will not be considered. The University of Luxembourg is committed to...
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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photopolymerization of the precursor. The practical work will be complemented by fluid mechanics computer simulations, including solutions employing machine learning, and theoretical analysis using Leslie-Ericksen
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Qualification: The candidate should possess a MSc. Degree or equivalent in Engineering, Computer Science, or related fields. Experience: The ideal candidate should have some knowledge and experience
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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research the Growing schools; Growing futures initiative, thereby contributing to the research program of the SciTeach Center team, led by Prof. Dr. Christina Siry. The PhD candidate will engage in