33 network-coding-"Chung-Ang-University" PhD positions at Technical University of Denmark
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energy modeling and analysis to be part of the Ports as Energy Transition Hubs (POTENT) Marie Sklodowska-Curie Actions Doctoral Network. The network will consist of 15 PhD candidates interested in
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. The position will involve development of codes/models simulating the nucleation and propagation of stress corrosion cracking in samples under low cycle fatigue conditions, as well as models for linking
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developing novel quantum photonic devices. Such quantum devices are central for quantum information networks, quantum computation, and quantum cryptography systems, and lie at the basis of a forthcoming
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computer science and control systems architecture, advancing all the above disciplines. Building energy flexibility is an important resource for balancing and load shifting in energy networks, especially
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, mathematical and programming contexts Your research will include extending and contributing to models and codes, including both high- and low-level programming languages, e.g. Python/Matlab to the development
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large national and international network and cooperate with research partners, public authorities, industry, and NGOs. We have state-of-the-art research facilities and Denmark’s only ocean- and arctic
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and dissemination activities within DTU courses and networks We are looking for a candidate who has: A Master’s degree in bioinformatics, microbiology, food science, data science, or a related field
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international network and cooperate with research partners, public authorities, industry, and NGOs. We have state-of-the-art research facilities and Denmark’s only ocean- and arctic-going research vessel
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, including a large international collaboration, offering excellent opportunities for networking with researchers and fellow PhD students, particularly in Sweden, Norway, and Portugal. Responsibilities and
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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non