6 phd-machine-learning scholarships in Italy

FAIROmics - PhD fellowship in Network-based algorithms for Knowledge Graph construction and analysis and application to the Omnicrobe knowledge graph.

(multiple omics, NGSeq, Neuroimaging, text data) with Machine Learning and AI techniques. He actually leads a group with 4 PhD students (1 ITN PhD student) 3 PostDoc Students, and 3 Research Assistants, with

FAIROmics - PhD fellowship in Artificial Intelligence-based tools for information retrieval from text documents - Automatic information extraction from scientific publications and other sources

(multiple omics, NGSeq, Neuroimaging, text data) with Machine Learning and AI techniques. He actually leads a group with 4 PhD students (1 ITN PhD student) 3 PostDoc Students, and 3 Research Assistants, with

PhD position on artificial muscles for soft robots - Department of Industrial Engineering (DII), University of Trento

, electronics, systems engineering, machine learning. A track record of project works Strong interest towards problem solving and experimental activity Fluency in English (written and spoken) Preferred

1 PhD position in Marie Slodowskwa-Curie Doctoral Network “NESTOR - Next generation high-speed optical networks for metro access”; research topic: “DC6: AI-based power saving and distributed sensing

by NESTOR is the combination of the advanced coherent pluggable transceivers with newly developed artificial intelligence/machine learning (AI/ML) algorithms, to allow the optical network to be

PhD position in Marie Slodowskwa-Curie Doctoral Network “NESTOR - Next generation high-speed optical networks for metro access”; research topic: “DC4: Digital twin of physical layer for short-reach

by NESTOR is the combination of the advanced coherent pluggable transceivers with newly developed artificial intelligence/machine learning (AI/ML) algorithms, to allow the optical network to be

PhD student - CaLiForNIA (11 positions)

quantum metrology and quantum machine learning. Expected outcomes: To improve the performance of variational algorithms, with application to quantum machine learning and quantum metrology. Requirements