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Field
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the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools. The goal is to extract governing equations directly from
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-on monitoring with cutting edge data-driven and physical based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored
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expert knowledge in a reusable format. Numerical Representation, Develop numerical representations of ship designs that are interpretable by machine learning algorithms and suitable for generative ai model
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such as malicious advertising, digital skimming and stealthy online tracking. By leveraging recent advances in machine learning and browser automation, the project aims to provide tools, techniques and
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, Department of Mathematics and Computer Science. The student’s working locations will be at Eindhoven University where the PhD candidate will be supervised by experts in combinatorial optimization, machine
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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools
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quantitative modeling; Strong expertise in programming, including proficiency in languages commonly used in data analysis and machine learning, such as Python; Excellent verbal and written communication skills
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this position, the chosen candidate will conduct research on how machine learning techniques or XAI can be leveraged by heuristic algorithms, or conversely, how heuristics can be enhanced by incorporating machine
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programming applications (e.g., experimental design, machine learning for science). We do so by bringing together a diverse team of PhD candidates who will focus on three key areas: Probabilistic and
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opening for a fully funded 4-year PhD position. We are looking for highly motivated applicants for a PhD student to strengthen our research team in an interactive and collaborative environment. Learning non