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attendance to two Dutch Research Schooles in Theoretical Physics over the course of the PhD. The position is embedded in a vibrant community of theoretical cosmology, gravitational-wave, and string theory
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Wrocław University of Science and Technology, Faculty of Pure and Applied Mathematics scholarship ID: WrocawTech -PHD [#26632] scholarship Title: PhD student scholarship scholarship Type: Student
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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Spectral Theory" at the Mathematical Institute at the Georg-August University of Göttingen. Your profile Master degree in mathematics or equivalent Research focus with an analytical profile in one
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Master’s degree (or other equivalent experience). Suitable backgrounds for these PhD positions include, but are not limited to, power engineering, industrial engineering and operations research, mathematics
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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of programming languages. The ideal candidate has an MSc in Computer Science or Mathematics and experience in one or more of the following areas: Theory of programming languages. Logical methods in
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, classical ecological theories and methods to mathematically model species interactions networks and communities across spatial scales. As part of the project, the student will extend these methods and make
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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph