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data analysis, alpha signal research, and strategy performance enhancement. While there is some overlap with the Quantitative Systematic Trader role, Quantitative Researchers typically focus more on
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sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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analysis methods. You will gain expertise in integrating experimental total scattering and high-resolution imaging data with artificial intelligence and atomistic simulation tools to overcome current
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experience in developing computational models and implementing models for computer simulations. Software development in C++ and/or Python is expected, and experience in model analysis and parameter
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extended from cloud solutions (such as OpenLLMetry), the research question is how to identify anomalies in collected information that can come from multiple AI services either invoked manually by users or by
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will focus on the development and application of innovative methods for data-analysis and integration, including AI based methods to combine the different types of data, using data from different case
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powerful ideas and tools at the intersection of topological band theory, symmetry analysis, and photonics. You will work on developing and applying these ideas to discover new topological phenomena, design
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an interdisciplinary team; Vegetation surveys, soil physical monitoring and carbon flux observations; State-of-the art analysis of spatio-temporal and ecological data; Collaboration with researchers and (BSc/MSc
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scientific data paper, to be released alongside or after the publication of the dataset; and Carry out a short, illustrative analysis to demonstrate the utility of the data (e.g. seasonal cycles). The student