16 optimization PhD positions at Delft University of Technology (TU Delft) in Netherlands
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Recycling (MPRR) group in the Department of Materials Science and Engineering (MSE) at TU Delft and contribute to advancing battery circularity by optimizing the recovery of critical raw materials in
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is to investigate the optimal configuration for utility-scale AWE farms, considering the physics, economics, and environmental impact. To determine the optimal scaling strategies for kite farms, a
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infrastructure, rolling stock and personnel that need to be aligned by efficient planning and replanning to optimize the allocation of the resources and maintain reliable operations in face of disturbances and
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In the FlexMobility project we propose a holistic
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coverage, (3) predicting the success of code changes based on test quality, providing developers quick feedback, and (4) designing autonomous agents that automatically refactor, prune, and optimize test code
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal network on climate action, particularly through the Climate Action Programme and its Climate Governance
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Systems department. You'll collaborate with an interdisciplinary team working at the intersection of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal
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to support coordinated decision-making for sustainable strategies in the port call? As a PhD student at TU Delft, you will leverage AI (i.e. optimization and machine learning techniques) to prepare ports
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these technologies can only read DNA fragments of limited length. We enable biological interpretation of these sequencing data sets by developing algorithms based on graph theory, discrete optimization and machine