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processing approach based on flow patterning to make meter scale LCEs of complex shapes and actuation modes. ALCEMIST builds on a tight synergistic collaboration between the Experimental Soft Matter Physics
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Applications should include: Curriculum Vitae including a list of publications or manuscripts in preparation (if applicable) and details of MSc training Cover letter Copies of degree certificates and statement of courses and marks (“relevé de notes”) Early application is highly encouraged, as...
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Applications should include: Curriculum Vitae Cover letter Contact information of two or three referees Research statement and topics of particular interest to the candidate (max 1 page) Early application is highly encouraged, as the applications will be processed upon reception. To ensure full...
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relevant state-of-the-art technologies. S/He will benefit from an active seminar program, international conference attendances, opportunities for professional growth. The project will be carried out in
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the impacts of various anonymization techniques from a business, legal and regulatory standpoint Designing and evaluating a reference process model to guide the implementation of legally compliant data
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proper customization, fine-tuning, extension to include context- or time-specific information and above all how to make all the process as much as automated as possible to support the automatic analytics
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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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the development of both, the quantum internet and distributed quantum computing. The objectives of this PhD thesis project are: (a) Demonstrate spin-photon entanglement with single colour centres in silicon carbide
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on multiscale analysis of brain disorders with a focus on Parkinson’s and Alzheimer’s disease, and epilepsy by combining experimental and computational approaches. For a collaborative project within the Institute
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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