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partners, coordinated by Fraunhofer ICT in Germany. The network will recruit a total of 17 doctoral candidates for project work lasting for 36 months. SPACER aims to develop new architectures for porous
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nature. To this end we will adapt existing enzyme architectures (i.e. naturally occurring cofactors and catalytic machinery) to conduct new catalytic tasks through a combination of chemical intuition and
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opportunities for embedding storage at multiple points within the HVDC architecture—on the AC side, DC side, or directly within converter submodules. The research will tackle several key technical, economic, and
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predictive or generative methods for materials discovery. Work with state-of-the-art neural network architectures applied to materials data. Publish scientific papers and present research results in top
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to engineer enzymes that can facilitate new reactions that were previously not known in nature. To this end we will adapt existing enzyme architectures (i.e. naturally occurring cofactors and catalytic
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. The team's main research areas are: Architectures for Autonomous Robots, Learning, Temporal Planning and Execution Control, and Algorithmic motion planning. RIS is composed of 8 permanent researchers, 4
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structures, designing appropriate benchmarks for the target domain, and determining which fusion architectures best balance performance, interpretability, and computational efficiency. The student will
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student to join team at TU/e. This project (SPACER) aims to develop new architectures for porous electrodes to improve the power density and energy efficiency of redox flow batteries (RFB), enabling
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benchmarks for these architectures. This will also guide hardware design for such devices and tackle crucial challenges in networked systems and entanglement transmission. Candidate’s profile Knowledge
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appropriate benchmarks for the target domain, and determining which fusion architectures best balance performance, interpretability, and computational efficiency. The student will collaborate closely with