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Field
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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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, execution efficiency, and live trading performance. What you can expect Modelling. Apply probability theory, statistical analysis, and machine learning techniques to analyze and interpret market behavior
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which can change over time, depending on neural activity. Incorporating the dynamics of the state-dependent delays will require the development of new tools for coupled oscillator theory in time-delayed
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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of communication networks Antenna theory Programming Skills: MATLAB, C/C++ Language Skills: Excellent written and verbal communication skills in English are required. French, German or Luxembourgish is a plus. We
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well as an independent researcher. Our work is interdisciplinary, international, and in-depth, but also practical. We offer a possibility to obtain broad and profound expertise, both theory and practice, in the field
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with all aspects of the project. Researchers will be encouraged to work on several aspects of the present project (for instance a specific experimental implementation and the underlying theory thereof
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in theory and in the lab, • have a strong interest in interdisciplinary work and commitment to work in Life Sciences, • be self-motivated and able to work independently, • and possess good knowledge
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/ Robust) Combinatorial Optimization, Game Theory, and Network Theory, as well as Artificial Intelligence. Potentially, scenarios could be simulated using agent-based, discrete-event, or other techniques
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cryptography, and will have an interest in interdisciplinary research. Knowledge of mathematics, particularly number theory, to the equivalent of first-year undergraduate level is desirable. We strongly