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the SCM section will fill a maximum of one PhD positions this year that can be on any of these topics. Machine learning for stochastic last-mile deliveries In recent years, stochasticity has received
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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Applications. The candidate will be embedded in the Massivizing Computer Systems (MCS) group, which focuses on research in distributed computing systems and ecosystems, and currently spans over 40 diverse people
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research (e.g., combining big data or machine learning with in-depth fieldwork) can also be pursued. Method selection and mastery are viewed as part of the PhD learning process, guided by supervisors and
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host a PhD day during which the students present their work and receive feedback from the department at large. Besides, we organize weekly lunchclubs during which both students and faculty can present
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for both autonomous and prosthetic applications. If you’re excited by all this, we encourage you to apply. The opening: In this project, you will develop: Detailed, large-scale computer models of
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diverse team of PhD candidates who will focus on three key areas: Probabilistic and differentiable algorithms for machine learning; Programming language implementation for high performance computing
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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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reference architecture for data visiting. This paradigm enables algorithms to securely access and process data within the environments where it resides, supporting federated learning for training machine
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considered an advantage. Technical Skills Proficiency in MATLAB, Python, and/or R. Experience with data science frameworks (e.g., AI, LLMs). Familiarity with machine learning (e.g., scikit-learn, MVPA, RSA