21 bayesian-object-tracking PhD positions at Delft University of Technology (TU Delft) in Netherlands
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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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. 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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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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). 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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theoretical modeling, numerical simulation, and experimental validation. Key objectives can include: Developing theoretical and computational models for friction-induced damping in joints and interfaces
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the material consumption and environmental impact of energy generation. This PhD project is part of the MSCA Doctoral Network AWETRAIN (Airborne Wind Energy TRAining for Industrialization Network). Its objective
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missions have revealed that some icy moons of the outer solar system have oceans beneath their icy crust. These findings have broadened the definition of habitability and placed these objects at the center
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algorithmic contributions in intelligent decision making. Apart from dealing with the scalability challenge inherent in modern AI applications, our group works on two main research objectives. First, we aim