16 bayesian-object-tracking PhD positions at Delft University of Technology (TU Delft) in Netherlands
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for personnel require alternative solutions, such as moving rolling stock maintenance to daytime on days or periods with less transport demand. The objective of this PhD project is to develop and demonstrate a
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modelling approach, and dynamic Bayesian Networks would be advantageous. Willingness to conduct research in a multi-national project team. Funding requirements: You cannot have resided in The Netherlands in
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the vehicle fleet and the multi-objective design of the mixed transporation network. Our key hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an
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Engineering, Physics, Applied Mathematics, or related discipline. Proven track record in numerical methods and computational fluid dynamics. Proven track record in machine-learning methods for computational
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to explore how governments balance economic development objectives with adaptation to accelerating physical climate risks, as households-voters decide where to live given their preferences for public
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). At EI you’ll find a welcoming and open atmosphere. We have a track record of nurturing talent at various academic levels and will give you all the support you need to evolve in your PhD. Our world-class
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to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track mutations in evolving populations
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. This involves dynamic engagement like walking, observing, and handling objects. The relationship between perceptual experiences and the dynamic structures of the multi-sensorial information generated by active
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superposition and entanglement to “large” objects that we usually think of as classical particles. This is exactly what you will do at TU Delft. As a PhD student in our teams, you will investigate how
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theoretical modeling, numerical simulation, and experimental validation. Key objectives can include: Developing theoretical and computational models for friction-induced damping in joints and interfaces