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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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experiments in controlled conditions aiming at simulating natural environments; 3) You will participate in arctic campaigns and use the methods developed to shed light on methane cycling in permafrost regions
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that may remove such constraints, leading to a fundamental challenge: the potential co-existence of genetically distinct clones, each supporting multiple stable cancer cell states. To understand the effect
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the detection of real versus AI videos. The focus has been from single disciplines (e.g., Masood et al., 2022), and typically technical in nature. By drawing on multiple modalities within video (i.e., visual
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest ideas using historical
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, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition Evaluation. Backtest
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theory, statistical analysis, and machine learning techniques to predict market behavior and generate alphas Execution. Create strategies to execute on modelling ideas under simulated competition
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: Prof. Dr. Steven Travis Waller, Chair of Transport modeling and simulation, and co-supervised by at least one additional professor, plus an international tutor of the CRC Requirements: excellent
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree