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models or methods (a.o. LCA, carbon calculation, material flow analysis, input/output analysis). Demonstrable experience developing and publishing research related to carbon calculation applications
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of mental and mood disorders. As a growing population lives in the urban environment, it is crucial for us to gain a more holistic understanding of how various elements of the built environmentaffect both
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and software for high-throughput retinal recordings, to conduct the respective measurements, as well as to further develop the data-evaluation and signal-analysis pipeline. The work will be performed in
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and policy choices Effects of intergovernmental fiscal transfers Fiscal decentralization and residential sorting The work might be based on theoretical modelling and/or empirical analysis. Profile
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are required to have: A completed PhD degree. Experience in machine-learning methods. Some skill in at least one of these topics: Large data sets analysis Statistics and uncertainty analysis (probabilistic
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger
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residual stress analysis. The objective will be to investigate these phenomena using simulation tools and via sophisticated experiments. Besides, the candidate is expected to supervise BSc-/MSc students
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analysis, and/or qualitative/quantitative methods. A demonstrated interest in topics pursued by the Chair team, such as knowledge creation, knowledge management, innovation processes, digital technology and
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signal processing (GPU based) Proficiency in data analysis using Python, Matlab, or similar Self-motivating, independent-minded scientific researcher, effective collaborator Excellent written and oral
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, serving as a basis for statistical evaluations of stone formats, brick bonds, and the like. Where applicable, you also perform geometrical analysis of vault geometries and traces of formwork. You interact