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Field
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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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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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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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for the efficient simulation of complex quantum systems, with applications to optimization and machine learning. The research will also address the development of advanced methodologies for quantum error correction
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of measures, optimal transport, partial differential equations, and variational approximation methods, with potential applications to optimization and machine learning. The successful candidate will work in an
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reinforcement learning methods for cooperative source localization in dynamic marine environments. Research will address distributed information fusion, decentralized coordination, and control with event
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validated nanoplasmonic sensing platform coupled with machine learning for the multiplexed analysis of prostate cancer (PC) biomarkers. Building upon our demonstrated ability to detect and classify ultra-low
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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
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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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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