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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
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workstreams, and the PhD’s will be working along senior staff to perform tasks in different workstreams, in strong collaboration with multiple international partners and fellow PhDs from all over the world. Key
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datasets, therefore, there will be a focus in the implementation of models for large volumes of data. The project will work in an exciting interface of statistics and machine learning and has the potential
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. The student will perform ‘big data’ analysis of patient cohorts including time-based evaluation of the impact of introducing CT-FFR as a national health intervention into a healthcare system. Exploratory
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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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the analysis of the complex data and cellular models (Big Data and Kavli Institutes). The DPhil will provide the student with multidisciplinary skills including specialized training in bioinformatics, genetic
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biodiversity or occurrence data (e.g., GBIF). Understanding of species distribution modelling or trait-based ecology. Interest or experience in applying AI or machine learning methods to ecological questions
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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