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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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focus will be on developing compact, efficient, and real-time LLM algorithms/hardware on the edge and developing demos for specific applications such as speech disorder therapy. Your responsibilities will
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) uses principles from systems neuroscience to develop reliable, low-power spiking neural networks and learning algorithms for implementation in a new generation of neuromorphic hardware. Both projects
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interdisciplinary, and together we contribute to science and society. Your role Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms
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will join an international and multidisciplinary team and will work in the project which aims to develop an innovative, modular, and partially bioresorbable wound filler for Negative Pressure Wound
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sufficiently “compact” (i.e. algorithmically small and computationally efficient) to enable incorporation in integrated PED models. The development of these compact models will involve collaboration with several
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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Vacancies PhD position on Stochastic geometric numerical methods Key takeaways Are you passionate about developing cutting-edge numerical algorithms at the intersection of geometry, stochastic
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will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work. We are a supportive team that will welcome
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Europe | about 1 month ago
manufacturing, development of machine learning algorithms and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen