Digital Molecular Magnetic Resonance Imaging

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181,89 

Series in BioEngineering

ISBN: 981976369X
ISBN 13: 9789819763696
Autor: Awojoyogbe, Bamidele O/Dada, Michael O
Verlag: Springer Verlag GmbH
Umfang: xxv, 348 S., 37 s/w Illustr., 134 farbige Illustr., 348 p. 171 illus., 134 illus. in color.
Erscheinungsdatum: 25.08.2024
Auflage: 1/2024
Produktform: Gebunden/Hardback
Einband: Gebunden
Artikelnummer: 3972300 Kategorie:

Beschreibung

Autorenporträt

Dr. Awojoyogbe O. B. has Ph.D. (1997) in Med. Phy. from Federal University of Technology Nigeria in collaboration with Institute of Biomed. Eng. & Med. Informatics, ETH Zurich & Univ. of Zurich. He won the 2003 Young African Math. Medal Award by African Mathematical Union. He is a professor of Physics and associate member of International Center for Theoretical Physics (ICTP), Trieste, Italy. His research field is Theory, Dynamics & Applications of Magnetic Resonance Imaging. He is a researcher of the Centre for Vaccine & Drug Development at the Federal University of Technology Minna. He served as the deputy director, Academic Planning (2008-2011) and the director, Academic Planning (2011-2015) at his University. His current research interests are the application of computational molecular magnetic resonance imaging for digital health, Quantum Artificial Intelligence (QAI)-and in particular, its subset research area Quantum Machine Learning (QML). Dr. Dada O. M. received his B.Tech. (Physics/Electronics with First class honours) from the Fed. Univ. of Tech., Minna, Nigeria in 2006, M.Tech. (Solid State Physics) and Ph.D. (Physics) from the same university in 2010 and 2016, respectively, under the supervision of Professor O.B. Awojoyogbe. He currently holds an academic position as a senior lecturer in the Dept. of Physics of the University. He has been involved in magnetic resonance modeling of biological systems and applications of computational molecular magnetic resonance imaging for digital health, Quantum Artificial Intelligence (QAI)-and in particular, its subset research area Quantum Machine Learning (QML).

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