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hydrogen in N-based semiconductors. We will use micro-photoluminescence, also time-resolved, Raman, (also in near-field regime), and auto-correlation measurements to probe quantum dot-like states. Where to
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18 Sep 2026 Job Information Organisation/Company UNIMORE Research Field Engineering » Computer engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA INDUSTRIALE | Italy | 7 days ago
. They will be involved in conducting experimental tests using mechanical testing machines and full-field strain measurement systems. They will collect and process the experimental data acquired during
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based on Artificial Intelligence, Machine Learning and Data Science for the modelling, analysis and interpretation of complex biomedical systems. Research activities will include the design and validation
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neuroscience, neuroimaging, machine learning and neurofeedback, providing an interdisciplinary research environment for candidates interested in the neural basis of emotion and consciousness. Job description: We
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data acquired by mobile road monitoring systems using heterogeneous sensors, such as images, laser profilometers, GPS, and audio data, will integrate machine learning, computer vision, and image analysis
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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bioengineering, data analytics, artificial intelligence, and machine learning. More information on our research strengths can be found at https://www.uta.edu/academics/schools-colleges/engineering/research
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors