João Ribau

ORCID iD
orcid.org/0000-0002-1018-6908
  • Also known as
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J P Ribau,

Sources:
João Ribau (2014-08-19)

João P. Ribau,

Sources:
João Ribau (2014-08-19)

João Ribau

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João Ribau (2014-08-19)

  • Country
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Portugal

Sources:
João Ribau (2016-01-24)

  • Keywords
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Optimization,

Sources:
João Ribau (2014-08-11)

Energy,

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João Ribau (2014-08-11)

Alternative Vehicles,

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João Ribau (2014-08-11)

Simulation,

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João Ribau (2014-08-11)

Life Cycle

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João Ribau (2014-08-11)

  • Websites
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IST

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João Ribau (2014-08-19)

Linkedin

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João Ribau (2014-08-19)

  • Other IDs
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ResearcherID: B-7694-2014

Sources:
Clarivate Analytics (2014-03-03)

Scopus Author ID: 44261708900

Sources:
Scopus to ORCID (2014-03-03)

Biography

João P. Ribau is a Scientific Researcher at the Sustainable Innovation Centre from ISQ (Instituto de Soldadura e Qualidade). Previously he was a Post-Doc researcher and an Invited Assistant Professor at the Institute of Mechanical Engineering (IDMEC) of Técnico Lisboa (IST), University of Lisbon. Received his Ph.D degree in Mechanical Engineering from IST, in 2014, with work in life cycle analysis and optimization of alternative road vehicles. In 2009, he received his M.Sc. degree in Mechanical Engineering, from IST, with work in analysis and simulation of electric and hydrogen consumption hybrid and electric vehicles. Participated in several international projects, under a LIFE Action Grant, and MIT-Portugal, and also national projects under FCT (Foundation for Science and Technology). He is the author of over 13 publications in scientific journals, one book chapter, more than 9 publications in international conferences, and 17 communications in international conferences. Currently he performs scientific research in the areas of optimization and decision in energy systems, interior air quality, and life cycle analysis. His scientific interests cover: • Water-energy nexus, Industrial symbiosis and sustainability, Circular Economy • Conventional and innovative powertrain technologies for vehicles (e.g. electric, hybrid, plug-in, fuel cell); • Life cycle analysis, and sustainability of energy production and conversion; • Optimization, metaheuristics, decision, and machine learning applied to energy systems and transports.
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