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to coordinate and conduct asset management and maintenance activities across infrastructure networks in the Netherlands. Together with our research partners from Next Generation Infrastructure network, the PhD
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Groups. Duties Your responsibilities will include some or all of the following: defining, negotiating, initiating and managing industrial activities including pre-Phase A, mission/architecture studies and
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inductive biases, we aim to identify key mechanisms that drive rapid learning in the visual system. The goal is to create a robust mechanistic neural network model of the visual system that not only mimics
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infrastructure data, and building visual tools to support the interpretation and communication of results. The work has a strong foundation in systems and network research, with an emphasis on DNS protocol
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of either a hands-on laboratory based project and/or a software simulation and modelling project. Both projects will contribute to the preparation of future space science missions. The laboratory-based
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verification software and ground support equipment; . implementing a scalable and modular digital twin from critical fixed assets to full production lines, as well as components critical to the overall launcher
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the influence of M-Wall radius on biomechanical outputs. 3. Methodology Transition the existing torque-driven simulations to a muscle-driven framework using OpenSim or similar software. Incorporate muscle
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the Office for Science Engagement and Oversight host the scientists and engineers that oversee the space missions, from study to end of operations. They define and develop the science operation systems
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and assisting in their resolution; contributing to the definition of technology development requirements and work plans for the Agency’s technology programmes; defining, initiating and managing R&D
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for molecular system. Recently, we reported a major breakthrough in using chemical reaction networks for so-called in chemico reservoir computing (Nature, 2024, 631, 549–555). This work demonstrated that self