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? As a PhD candidate, you will develop an integrated modelling framework to analyze the Water-Energy-Food (WEF) nexus in Controlled Environment Agriculture (CEA), including greenhouse and vertical
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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stakeholder requirements into façade design interventions. Design, prototype and evaluate biophilic façade concepts integrating vegetation, water retention, circular materials and/or habitat-supporting features
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, material, technical and stakeholder requirements into façade design interventions. Design, prototype and evaluate biophilic façade concepts integrating vegetation, water retention, circular materials and/or
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integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly affected by environmental and operational conditions. This project aims to tackle
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microfluidic contact lenses capable of collecting and analysing tear fluid in a controlled and non-invasive manner. By integrating microchannels, microvalves, and sensing functionalities into soft hydrogel
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liquid biomarkers for leukodystrophies by integrating multi-omics data from patient plasma and iPSC-derived human disease models. You will investigate disease-related molecular signatures using
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robotic platforms, integration of optical and imaging-based feedback, and development of modeling and control strategies for operation in complex biological environments. Particular attention will be given
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decisive importance for lifetime performance modelling. The key challenges are the robust integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly
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for lifetime performance modelling. The key challenges are the robust integration of a sensor system in the structure and the analysis of measured signals, which are typically strongly affected by environmental