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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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across copper–silicon interfaces. You will work with characterization methods including SEM, EBSD, XRD, thermal conductivity analysis, and thermo-mechanical testing. The project also includes
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levels. A central component of your work will be the collection and analysis of qualitative empirical data. The empirical work will be based on in-depth case studies of firms operating within the Danish
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Cycle Assessment (LCA), Input-Output analysis (I/O), and Material Flow Analysis (MFA) with scientists’ contribution to IPCC and IPBES assessment reports, the International Resource Panel, active
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reflecting airway tissues. Your tools: Interdisciplinary receptor analysis: you will work with glycan arrays, engineered glycans, influenza binding assays, and—depending on expertise—molecular biology
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heterogeneity in malignant brain tumors. The primary responsibilities for this position will be focused on analysis of single-cell transcriptomic and epigenomic datasets with the goal of identifying cell types
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housing estates in Flanders and analyse co-creative and ‘futuring’ methods for co-design. Together with residents and other relevant stakeholders, and inspired by the analysis of destigmatising practices
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; Interest in interdisciplinary environmental assessment and offshore renewable energy systems; Experience with programming and/or data analysis (e.g. Python, R, MATLAB, GIS); Excellent written and spoken
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responsibilities involve: Carrying out a thorough analysis of existing literature on tVNS and cognitive functions Designing and carrying out new experiments to examine whether tVNS enhances cognitive functions
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for the analysis and control of large classes of nonlinear and high-dimensional systems, such as air flow dynamics in complex greenhouse environments. In this PhD project, you will explore and conduct research