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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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analysis using Leslie-Ericksen theory for liquid crystal flow as well as theory for viscoelasticity of polymeric liquids. Both in the practical and simulation/theory work you will be guided and supported by
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analysis of future 6G communication networks that are capable of supporting new services for digital ecosystems. Use cases of interest include Security and Efficient Wireless Communication Solutions, IoT
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datasets such as the Luxembourg Parkinson’s Study and different prodromal cohorts for neurodegenerative diseases are ready for analysis. The doctoral researcher will: Conduct research that will compose a PhD
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in a number of the following topics: Turbulence modeling with wave propagation simulations Modulations used in optical wireless communications Data Analysis and Management Implement and open-source
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into cross-seeding between microbiome-borne amyloidogenic proteins and human proteins implicated in neurodegenerative disea Your responsibilities: The doctoral candidate will perform computational analysis
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innovative and creative research in theoretical quantum physics, including mathematical modeling and phenomenological and numerical analysis. The PhD student should display scientific ambition and target
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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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for Digitalization. This project aims to advance privacy-preserving techniques for data analysis and task automation, ensuring robust protection of sensitive information. The focus will be on developing
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within the project Data analysis and publications in peer-reviewed scientific journals and presenting at international scientific conferences. Contribute to and support (on-going and future) research