40 bayesian-object-detection PhD scholarships at Delft University of Technology (TU Delft) in Netherlands
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of these are highly localized. Localized corrosion under a coating is however typically difficult to detect visually. Electrochemical Noise (EN) can detect this at an early stage and is not limited by geometric
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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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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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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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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partners: ASML and DCODIS (a start-up). This is technically challenging applied research with as main outcome a proof-of-concept tool that allows developers to quickly find and fix software errors including
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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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enhancing maintainability and performance while also reducing technical debt and facilitating bug detection and bug fixing. The main modules include (1) capturing practitioners' experiences and expectations
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aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object. Unlike traditional robots