Lehmann

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Thomas Lehmann

former Postdoc

Stay period:Jan. 1, 2012 - Dec. 30, 2019

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Thomas is a Theoretical Physicist with a focus on numerical modelling of quantum transport in nanostructures and molecular electronics. He studied Physics at the TU Dresden and joined this chair in July 2012 for his Diploma thesis on 'Ab initio and transport models for graphene nanoribbons under mechanical stress and defects' in the group of Dmitry Ryndyk. He obtained his PhD for his work on 'Computational Modelling for Planar Molecular Electronics', in cooperation with the International Max Planck Research School and the International Graduate School iEGSEMP: Korea with research stays at POSTECH (Korea), CEMES-CNRS (France) and UNIST (Korea). Since becoming interested in Machine Learning methods, he has been awarded a Data Science Fellowship at ASI Data Science in London. He has been working for easyJet to develop a dynamic pricing model for seat allocations by predicting the time-evolution of customer demand. He is now looking into applications of Machine Learning models for materials science.




Publications

pages: 1 2 >
Lehmann

Google Scholar Linkedin

Thomas Lehmann

former Postdoc

Stay period:Jan. 1, 2012 - Dec. 30, 2019

Download vCard


Thomas is a Theoretical Physicist with a focus on numerical modelling of quantum transport in nanostructures and molecular electronics. He studied Physics at the TU Dresden and joined this chair in July 2012 for his Diploma thesis on 'Ab initio and transport models for graphene nanoribbons under mechanical stress and defects' in the group of Dmitry Ryndyk. He obtained his PhD for his work on 'Computational Modelling for Planar Molecular Electronics', in cooperation with the International Max Planck Research School and the International Graduate School iEGSEMP: Korea with research stays at POSTECH (Korea), CEMES-CNRS (France) and UNIST (Korea). Since becoming interested in Machine Learning methods, he has been awarded a Data Science Fellowship at ASI Data Science in London. He has been working for easyJet to develop a dynamic pricing model for seat allocations by predicting the time-evolution of customer demand. He is now looking into applications of Machine Learning models for materials science.




Publications

pages: 1 2 >