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Field
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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
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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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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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analytics (statistical models, machine learning, uncertainty quantification) to monitor and predict cycling travel conditions from various perspectives (safety, crowding, travel time, comfort, etc
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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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-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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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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Christmas and 1 January; multiple courses to follow from our Teaching and Learning Centre; a complete educational program for PhD students; multiple courses on topics such as time management, handling stress
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scans and use advanced data-driven methods, including artificial intelligence (AI) and machine learning to improve outcome prediction and patient stratification. deepen our understanding of the etiology