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, and multiphysics coupling, which are difficult to capture in-situ with traditional methods. Typically, imaging is possible in such application, but full mechanical characterization is not. Our vision is
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computational research. In particular: • A high-quality imaging platform • A dedicated biocomputing hub that guarantees reliable data storage, management, and advanced analytical capacity. Our laboratory is
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their response to different environments, we try to replicate different environments under controlled laboratory conditions. By systematically confronting users with such environments, we aim to identify and
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analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN implementation topology and its energy efficiency
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8 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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Unravel the complexity of valve disease in heart failure using Digital Twin technology. Help transform how cardiologists decide when and how to treat patients through personalized computer
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characterizing defects such as dislocations Applying generative models (e.g., GANs, diffusion models) to augment microscopy datasets Investigating domain adaptation techniques across different imaging modalities
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to recognize and critically reflect on the influence of both linguistic and multimodal forms of communication. For example, how words like “riot” versus “demonstration” frame the same event very differently
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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. Identify the key factor affecting texturization processes and study the relationship between food structure and textural characteristics at different scales using spectroscopic, microscopic, rheological