15 modelling-complexity-geocomputation PhD positions at University of Twente in Netherlands
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model of the non-transparent structure. Using this approach, we aim to build a microscope that can look inside very complex materials without loss of image quality. Your role is to build flexible 3-D ray
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Vacancies PhD position on attacks against large language models (LLMs) Key takeaways This project will investigate attacks on large language models (LLMs), a major recent development in artificial
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, and complex mechanical designs. The MagHeat project introduces groundbreaking innovations to overcome these barriers, including a novel stationary permanent magnet assembly and the use of a high
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robotic platforms, integration of optical and imaging-based feedback, and development of modeling and control strategies for operation in complex biological environments. Particular attention will be given
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manufacturing economy by enabling repair and remanufacturing through advanced Additive Manufacturing (AM) technologies. Instead of following the traditional linear model of “produce–use–discard”, ADD-reAM focuses
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. Instead of following the traditional linear model of “produce–use–discard”, ADD-reAM focuses on extending product lifetimes, reducing waste, and lowering dependency on virgin raw materials by embedding AM
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innovative programme within the Chemical Science & Engineering curriculum at the University of Twente. It aims to equip students with the skills and mindset needed to tackle complex global challenges such as
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simulation models. Often building on gaming technology, these systems offer an immersive environment where players can explore the real world, adapt it, and inspect simulated effects. Geospatial digital twins
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in motor control by computational modelling and non-invasive brain stimulation. The focus of this project will be on advanced versions of transcranial alternating current stimulation (tACS), targeting
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will