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to develop a new generation of traffic prediction methods, combining traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where
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, enabling more frequent, scalable, and data-driven asset management. The project will investigate the integration of Axle Box Acceleration (ABA), Laser Doppler Vibrometry (LDV), and Track Geometry (TG) data
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-driven researcher. We are looking for the following: MSc degree in chemical engineering or similar field (essential). Strong background in chemical process modelling and design (essential). Prior
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embankment health through the integration of multiple monitoring technologies, enabling more frequent, scalable, and data-driven asset management. The project will investigate the integration of Axle Box
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), Computer Science (Machine learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development
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into different stages of optimization, both as priors and as feedback after optimization outcomes. Information This PhD project is a part of the CoRDS project – Confident Data-Driven Decision Support (https
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“Health Passport” system for railway tracks on bridges, enabling data-driven condition assessment and supporting more efficient maintenance and asset management strategies. The project aims to develop a
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is decentralized or only partially observable? Depending on the research direction, you may employ techniques from mathematical modelling, machine learning, uncertainty quantification, distributed
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deterioration. This PhD project addresses this challenge by developing a novel “Health Passport” system for railway tracks on bridges, enabling data-driven condition assessment and supporting more efficient
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, and materials science. In this PhD project, you will: § Develop an experimental platform to study 3D self-organization of active particles. § Fabricate and characterize light-driven microswimmers