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Field
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available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and
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composites toward a new generation of smart structures with built-in sensing and intelligence. By integrating optical fibre sensors directly into composite materials and combining them with physics-based
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Research Project Nucleic acids are potent triggers of immune responses. The presence of virus-derived RNA or DNA molecules activates nucleic acid sensors inside the host cell, which stimulates protective
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workflows between field acquisition teams, data center staff, and research collaborators to ensure seamless data transfer and integration. -Support daily operations of servers, networks, storage, and cloud
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. You will work at the frontier of: Vision-Language Models (VLMs) Generative AI and multimodal foundation models Weak supervision and open-set recognition Multi-modal sensor fusion across viewpoints and
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Infrastructure? No Offer Description Grantor: European Union HORIZON Marie Sklodowska-Curie Action (MSCA) Joint Doctoral Network Project portal: https://www.eu4greenfielddata.eu/ Highlights Duration: 1 Sep, 2026
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SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing for Fibre Optic Links’
to support growth in sectors such as healthcare, telecommunications, advanced manufacturing, and sensor networks. The position is available at Tyndall National Institute in Cork. Project Description
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, biomarkers, omics data, registry data, and sensor data such as CGM measurements (continuous glucose monitoring), activity trackers, and other wearable sensors. Contribute to grant proposals and projects
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Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and clinical medicine to transform diagnosis and treatment of adrenal diseases. Digital medicine is
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Control is successfully conducting research as well as postgraduate and undergraduate education within areas such as autonomous systems, complex networks, data-driven modeling, learning control