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training schools. As a participant of the project, the PhD student will become part of a team at DTU with expertise in design, fabrication and characterization of wide bandgap semiconductor photonic devices
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network, including 3 training schools. As a participant of the project, the PhD student will become part of a team at DTU with expertise in design, fabrication and characterization of integrated diode laser
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innovation. This PhD opportunity is part of the Marie Skłodowska-Curie Doctoral Network UP2MEM, a European training network committed to advanced MEMbrane technologies for water and food industries. Through
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. students in the doctoral network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise
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schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing. The activities within
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the other 14 Ph.D. students in the doctoral network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and
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the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing. The activities within the project will benefit from synergies with other
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network, including 4 training schools and two workshops. As a participant of the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing
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The recruited researcher will have the opportunity to work as part of an international, interdisciplinary team of 17 doctoral candidates, based at universities and industrial firms throughout Europe. She/he will
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following research areas: multi-modal input, continual learning, and adaptive methods for task planning and execution in real-world environments with autonomous agents. You will be a vital part of the Visual