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- Fondazione Bruno Kessler
- Politecnico di Milano
- Università degli Studi di Brescia
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- Alma Mater Studiorum - Universita' di Bologna
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- Dipartimento di Ingegneria dell'Informazione - Università degli studi di Padova
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infrastructures, with a focus on light electric vehicles, machine learning and artificial intelligence, SCADA data analysis, diagnostics and monitoring of renewable energy systems, electrical measurements, and
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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of qualifications. Interview on: a) discussion on submitted qualifications and publications b) assessment of skills and knowledge in the field of: - Artificial Intelligence, Machine Learning and generative models
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The position requires developing and implementing machine learning models
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Science Research unit: https://genomics.iit.it/ ESSENTIAL REQUIREMENTS PhD in computational biology, machine learning, bioinformatics, physics or related fields; High proficiency level in programming
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simulation, Digital Twins, Big Data, IoT/Web of Things, HCI, Edge/Cloud Computing, AI, Computer Vision, Machine/Transfer Learning, Computer Science Education, Computational Thinking, Formal Methods, Logic, Web
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of renewable energy sources, with a focus on wind and solar power plants. By integrating meteorological observations, high-resolution numerical models and machine learning algorithms, highly accurate forecasts
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of data collected from smart bricks and smart mortars using machine learning and artificial intelligence techniques for damage identification and classification, detection of failure mechanisms and attained
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). Desirable assets are: • Machine learning, deep-learning, artificial intelligence, advanced statistical inference; • A solid record of research activities, including relevant publications in international peer
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include the development of machine learning models and decision-support systems for crop monitoring,early detection of plant stress and diseases, prediction of environmental performance and digital