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Bio-inspired controllers, AI and data for Real-Time Prevention”, operation code NORTE2030-FEDER-01179400, financed National Innovation Agency, S.A. (ANI), through the European Regional Development Fund
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Software Engineer to join the EPSRC-funded National EdgeAI Hub for Real Data. This role will contribute to Work Stream 5, focusing on the validation of data-sensitive applications in edge AI, addressing
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University Centre for Energy Efficient Buildings, Czech Technical University in Prague | Czech | 10 days ago
line. Start date is by agreement. We will contact applicants on an ongoing basis. Where to apply Website https://jobrxiv.org/job/embedded-developer-researcher/?utm_source=euraxess Requirements Additional
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achieved: The work plan will consist of: i) improving the development of computational tools and optimizing the parameters of the best tool for detecting fall risk situations in assisted walking with
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analysis, as many observed phenomena cannot be adequately modeled by stationary processes. The NOMOS project aims to develop a new generation of nonstationary models and algorithms for analyzing various
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OFFER PREVIOUSLY PUBLISHED ON THE EURAXESS PORTAL (OPEN FROM 29 MAY TO 12 JUNE 2025), INTENDED TO CORRECT INFORMATION CONTAINED IN THE ORIGINAL ANNOUNCEMENT. The collaborative laboratory Value for Health
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. The project aims to address the challenges in pooling inference, by developing and implementing either exact or asymptotically exact Monte Carlo algorithms in collaboration with the Department
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: Have demonstrable experience in the use of machine learning algorithms applied to engineering phenomena, especially in the areas of structural engineering; Have applied knowledge in the finite element
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advance the development of the Tool’s algorithms and functionality. As a key innovative component of D-Suite, this open-source tool will achieve wide industry visibility, and will be formally evaluated by
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learning, and generative AI Design and implement algorithms for quantum-inspired and quantum-enhanced generative models Investigate theoretical foundations of tensor networks, entanglement, and collapse