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Field
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Why apply? Generative AI and large-language models (LLMs) are about to turn computer-aided engineering into true human–AI co-design. In the new MSCA Doctoral Network GenAIDE we team up with Honda
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, including scenario-based and tube-based approaches, to ensure reliable operation despite significant uncertainty in weather, demand and energy prices. In collaboration with UK Power Networks and SSE Energy
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properties of the extracted compounds, (iv ) scale-up the optimized extraction process for potential industrial application. This is a unique opportunity to contribute to sustainable food innovation while
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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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, mathematics, computer science or similar; Solid mathematical and analytical skills, including signal processing, optimization, or information theory; Experience in programming, e.g., in C++, Python or Matlab
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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global academic and industry network🌟 Who we’re looking for:- A curious, ambitious researcher with a strong background in signal processing, wireless systems, mathematics (statistics, linear algebra
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competences within computational modelling, optimization and integration of thermal energy storage technologies – such as large water pits and phase change material storage. You will work with colleagues, and
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 3 months ago
Mathematical Modelling of kinetic and thermodynamic performance of material to optimize the production process. The research has significant real world impact. It is expected to deliver a step change in
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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary