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highly motivated Ph.D. candidate. The background is preferably in electrical engineering, computer architecture, control, computational neuroscience, or similar fields. Unconventional backgrounds supported
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will explore advanced THz front-end architectures and evaluate their applicability in real-world scenarios. Key objectives include: Exploring use cases and front-end architectures in the 300–500 GHz
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is
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channels to reach a broader audience. As a PhD candidate or Postdoctoral researcher, you will be embedded in TU/e innovation Space and the Sustainability Office at TU/e and actively contribute
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. Map, document and analyse settlement typologies in selected peri-urban sites, using field information, spatial data, and architectural methods and techniques. Investigate the role of policy, governance
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of 1) system-level co-design of architecture, functionality and performance, and 2) leverage system models to monitor and understand operational behaviour of systems and act upon irregularities
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iii) developing socially just heat adaptation strategies, that are context and conflict sensitive. This PhD position is situated at the Faculty of Architecture and the Built Environment, Department
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managers, and professors shares the ambition to create smart, sustainable, and equitable mobility systems. In our 12 collaborative labs we apply advanced technologies such as sensing, data analytics
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and optimize device architectures using finite element simulations. Fabricate prototypes using 3D printing, and cleanroom technologies. Implement test setups and assess devices performance through