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-101094250, designated by IMAGINE - “Next generation imaging technologies to probe structure and function of biological specimen across scales in their natural context” funded by the European Commission
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and quantitative image analysis. The results will be integrated with previous data, contributing to the characterization of the mechanisms underlying mitochondria–lysosome communication and to
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will be experimentally tested, with an analysis of the resulting fatigue life and crack trajectory. The tests will be monitored using a Digital Image Correlation (DIC) system, enabling the determination
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Research novel deep learning models for anatomically structured EGGIM estimation.; • Develop methods for image-level and examination-level reliability
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learning-assisted computational pipeline for the automated detection of point defects in atomic-resolution scanning transmission electron microscopy (STEM) images. Using monolayer MoS₂ as a model system, the
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) Lightweight DLT-backed credential gateway architecture for constrained IoT devices ii) Prototype implementation based on existing SSI and DLT components iii) Scalable evaluation environment and a complete
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and develop the corresponding biasing network.; • Fabricate and assemble a proof-of-concept prototype on a glass substrate.; • Experimentally characterize the electromagnetic performance
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demonstration of a prototype in a laboratory environment, as well as integration into real use cases ; - Writing project documentation and scientific publications. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND
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; André Venturoti Perrotta; Paula Alexandra Gomes da Silva. IV - Work Plan / Goals to be achieved: The scholarship will support the building of a functional prototype where an intelligent AI agent is
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: Previous experience in biomedical image and signal processing; Previous experience in technology transfer processes in biomedical tecnologia.; Minimum requirements: MSc Degree - Enrollment in a PhD program