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Field
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look for correlations between transient phenomena in the ionosphere and seismic events. The successful candidate will develop and apply state-of-the-art machine learning techniques to enhance
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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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). 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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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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regression, survival analysis, classification, and machine learning techniques. Familiarity with bioinformatics workflows, reproducible data analysis, and high-performance computing (HPC) environments
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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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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
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expertise in biodegradable drug-delivery technologies (https://www.mgshell.com ) and Politecnico di Milano’s strengths in microfabrication and digital machining. Secondments include MgShell (3 months
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive
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nanocrystals, hybrid perovskites and 2D materials. Development of new data-driven approaches for studies of optoelectronic properties using EM, including machine learning / machine vision algorithms. The balance