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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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-driven workload estimation, intelligent release planning, and explainable decision support. The PhD will operate across two worlds: The University of Twente — advancing scientific models, algorithms, and
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battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The four PhD students will work on topics related to: Physics-based battery
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Vacancies PhD Position in Real-Time Multimodal Construction Transport Optimization & AI-Driven Decision Support (MULTIPLIER Project) Key takeaways We are looking for a highly motivated and curious
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solutions, including training algorithms and preparing solutions for clinical implementation. Assess the impact of your workflow solutions after implementation, determining whether the expected improvements
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The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing. As machines
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» Algorithms Mathematics » Computational mathematics Mathematics » Mathematical logic Researcher Profile First Stage Researcher (R1) Country Netherlands Application Deadline 16 Feb 2026 - 22:59 (UTC) Type
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are currently looking for a highly motivated PhD candidate to join our team (18 months with the intention for extension to up to four years, full time, fully funded) at the Hydrology and Environmental Hydraulics
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for problems that are 'quantum' NP-hard (QMA-hard). What you will do Quantum algorithms and complexity theory; Quantum error correction protocols; Quantum information theory; Classical representation of quantum
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demonstrate the next-generation algorithms and models for the future Battery Management System. The four PhD students will work on topics related to: Physics-based battery modelling and model identification