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Manuel DOBLARE-CASTELLANO

Fellow // 2018

Full Professor Structural Mechanics, Department of Mechanical Engineering, University of Zaragoza, Aragón Institute of Engineering Research (I3 A), University of Zaragoza, Aragón Institute of Research in Health Sciences (IISA)
 


  mdoblare@unizar.es

  https://www.mdoblare.com


Inaugurated for:

his frontier position in structural biomechanics and his leadership in multidisciplinary education and transfer of research into industry.

Short CV

Manuel Doblaré is mechanical engineer (University of Seville) and PhD from the Polytechnic University of Madrid. He was lecturer and associate professor in Sevilla and Madrid, and, in 1984, he got the chair of Structural Mechanics at the Department of Mechanical Engineering, University of Zaragoza. He was visiting scholar at the universities of Southampton and New York and visiting professor at Stanford.

Dr. Doblaré was head of Department, Dean of the School of Engineering, Director of the Aragón Institute of Engineering Research and Scientific Director of the Spanish Networking Center on Bioengineering, Biomaterials and Nanomedicine. From 2011 to 2016, when he moved back to his chair in Zaragoza, Prof. Doblaré was selected as Scientific Director and CEO of Abengoa Research, the Corporate Research Center of Abengoa, a multinational company, world leader in renewable energies and sustainability.

Prof. Doblaré has published more than 200 papers in ISI journals and has supervised more than 35 PhD Theses. Fruit of that work, he received the individual prize for excellence in research from the Aragón Government and was distinguished with the “Honoris Causa” Doctorate by the University of Cluj-Napoca (Romania). He was also elected member of the World Council of Biomechanics and permanent member of the Spanish Royal Academy of Engineering.

Dr. Doblaré’s research interests are in computational solid mechanics and multiscale and multiphysics with applications in biomechanics and mechanobiology. In the last years, he has focused his research on model reduction and the combination of machine learning techniques and physical models.
 

Manuel