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Field
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tools for assessing material lifetime and performance, in connection with the data architectures developed within the REFFRACTEUR project. All modelling developments will be implemented in the open-source
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. You will be based in the Department of Management in the Built Environment at the Faculty of Architecture and the Built Environment, TU Delft. The project will be supervised by Daniel Hall, Eleni
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Technologies (Ireland); and a network-wide training program. Responsibilities and qualifications The PhD scholarship is on the topic of “Carbon-Aware Neural Architecture Search (NAS)”. Most AI models are built
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candidates hold a Master’s degree in Informatics, Mathematics, or a related field, and possess strong expertise in linear algebra, GPU architectures, and programming in C++ and Python. This is a 100% TVL E13
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real‑time anonymization in practice. The researcher will develop hardware‑aware neural architectures with hardware-in-the-loop optimization and efficient inference strategies to meet the computational
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Analysis and Architecture (LAAS) within the MechaBioFluidics team, which has extensive expertise in microfluidics and microfabrication (a technology platform of the national ReNaTech network), both located
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, through optical methods, FRET, microfluidics, microfabrication and surface biofunctionalization. 2. Main responsibilities The candidate will have the following responsibilities: i) carries out scientific
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information theory: designing and analysing error-correcting codes, establishing fundamental performance limits, and building practical decoding algorithms and architectures. Your research will sit at
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
; Autonomous ships and remote control. The research partners are the Norwegian University of Science and Technology (NTNU), University of Oslo, Oslo School of Architecture and Design, Royal Norwegian Naval
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to the objectives of technological sovereignty, methodological innovation, and the structuring of CCI ecosystems by proposing a scientific framework for mastering the use of generative AI in cultural and heritage