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the host team. The PhD project will focus on developing new post-processing methods using neural networks, starting with coupled PLIF-OH/PIV and PLIF-NO/PIV diagnostics, then extending to the analysis of all
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
analysis of parameterized finite‑element models. By integrating data‑driven and FEM‑based approaches, a hybrid model is developed that accurately represents physical relationships while providing real‑time
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: Archeometallurgical analyses SEM-EDS microscopy and chemical composition analysis of iron artifacts, slags, and ores GIS spatial analysis, statistical analysis, and network analysis of iron production Other
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or other large-scale biological data), using statistical methods, pathway/network analysis or machine learning. The candidate will conduct integrative analyses of biomedical datasets, focusing on single-cell
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young versus older adults using EEG. Analyses will focus on Event-Related Potentials, time-frequency analysis and functional connectivity measures within a cognitive-motor dual task paradigm. To enhance
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Systems (GIS) and large-scale spatial and environmental pattern analysis. They will also be responsible for participating in soil sampling teams and in the design of large-scale monitoring networks. With
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and chemical composition analysis of iron artifacts, slags, and ores GIS spatial analysis, statistical analysis, and network analysis of iron production Other Participation in ensuring operational and
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and energy-efficient manufacturing in the Greater Region. Modern industrial production systems rely on networks of sensors to monitor processes, energy consumption, material flows, and environmental
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statistical analysis. Supervise and mentor junior researchers, PhD candidates, and technical staff within the team. Manage the reporting process and scientific production of the research activities, such as
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software