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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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this initiative, 15 early-stage doctoral candidates (DCs) will be trained through a comprehensive, interdisciplinary program spanning material science, device physics, computer architecture, hardware prototyping
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be used to calculate the theoretical maximum energy efficiency of an AI algorithm for a specific architecture/accelerator and will help you optimise the energy consumption of AI algorithms. To make
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; optimisation, systems and control; and sustainable chemical engineering and biotechnology. Qualifications The ideal PhD candidate for this position is a talented engineer with a Master's degree and has
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into recycle, reuse, or manual-review streams. Iterative hardware-in-the-loop trials will drive rapid prototyping of gripper architectures and autonomy frameworks, thereby establishing a scalable blueprint for
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for tabular-native models. This can involve, for example, studying new TRL model architectures, serialization and tokenization techniques, among others. A strong interest and background in AI and/or NLP
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at universities, such as thesis supervision and assisting in courses. Requirements Desired profile A master degree in computer science, artificial intelligence, human-computer interaction or similar. Self-drive