21 software-defined-network-phd PhD positions at Eindhoven University of Technology (TU/e)
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this PhD project, you will investigate the time-bin entanglement generation and detection, architecture, and protocols of entanglement routing in a multi-node quantum network. Additionally, you will
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to work at the intersection of computational modeling, vascular biology, and clinical innovation? The ThromboRisk Doctoral Network offers an exceptional opportunity to join a European-wide research
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you our next PhD candidate in statistical data integration? Are you
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We are seeking a motivated PhD candidate to develop innovative visual
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. This project focuses on fundamental algorithmic questions on geometric networks and, in particular, on geometric intersection graphs: graphs whose nodes correspond to disks or other objects in the plane and that
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particular connectivity within and between complex system? Would you like to apply efficient AI techniques for adaptive configuration and optimization of industrial communication networks to enhance
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stability of the material. As PhD candidate you will develop a model framework which dynamically links the pore space dynamics with the pore flow phenomena and vice versa. The topics you will delve
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efficient operation. In this PhD project, you will design and develop integrated photonic terahertz sensors for Electron Cyclotron Emission (ECE) measurements in fusion reactors. These sensors will enable
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mental illness. We are hiring a technically strong and socially motivated PhD candidate to join our collaboration between Eindhoven University of Technology (www.tue.nl ), TNO (www.tno.nl ), and project
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? Do you like studying physical systems with wide impact in society? Then, this PhD position is ideal for you! Information This PhD project aims to develop advanced fluid models for low-temperature