This course teaches the students practical skills needed for solving modern physics problems by means of computation. A number of examples illustrate the utility of numerical computations in various domains of physics.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Prof. Oleg Yazyev (Олег Язев) was born in Simferopol, Crimean peninsula. He obtained his degree in chemistry from Moscow State University in 2003. He then joined Ecole Polytechnique Fédérale de Lausanne (EPFL) completing his PhD thesis in chemistry and chemical engineering in 2007. Next two years he has spent as a postdoctoral fellow at the Institute of Theoretical Physics (ITP) and the Institute for Numerical Research in the Physics of Materials (IRRMA) of the same institution. In 2009-2011 he was a postdoctoral fellow at the Department of Physics of the University of California, Berkeley and the Lawrence Berkeley National Laboratory. In September 2011 he started an independent research group supported by the Swiss National Science Foundation professorship grant. In 2012 he was awarded an ERC Starting grant. His current research focuses on theoretical and computational physics of the recently discovered Dirac fermion materials with strong emphasis on their prospective technological applications. ResearcherID profile of Oleg Yazyev
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, w
Est adipisicing consectetur laboris ipsum voluptate esse occaecat tempor ea ut. Pariatur mollit adipisicing elit ex minim labore incididunt laborum consequat id laborum enim. Veniam enim cupidatat exercitation adipisicing occaecat. Eu proident culpa laboris magna et eu ea consequat voluptate mollit deserunt labore duis anim. Consectetur ea labore incididunt qui. Eu tempor quis ullamco irure nulla minim nulla.
Magna dolor amet incididunt non deserunt culpa Lorem sint irure anim Lorem ex magna. Do pariatur cupidatat cupidatat fugiat nostrud proident voluptate est magna. Deserunt fugiat velit aliqua do aliquip sint enim amet ut irure anim. Excepteur minim proident laborum ipsum mollit ullamco labore enim officia irure nostrud veniam sit excepteur. Voluptate Lorem officia enim ex sint ipsum aliquip adipisicing.
Occaecat laboris officia nulla enim et. Officia enim mollit ut fugiat tempor fugiat excepteur commodo enim. Adipisicing ad tempor in Lorem in fugiat reprehenderit anim laborum sunt. Sunt dolor eu aliquip culpa in nulla. Est ad elit ex commodo esse id qui elit. Labore et qui ex exercitation ad sint qui incididunt.
Esse esse commodo velit velit ea proident ipsum ullamco veniam nulla do. Elit proident in nostrud proident adipisicing anim consectetur nisi et eu nulla. Officia cillum adipisicing dolor dolore anim aliquip cillum consectetur consequat. Occaecat sint aliquip pariatur fugiat nulla consectetur consequat quis reprehenderit laboris Lorem adipisicing.
Id magna nulla deserunt eu deserunt veniam duis. Ullamco nostrud consequat ipsum amet. Sint ipsum pariatur eiusmod ad consequat consequat veniam aliquip tempor culpa. In et sint voluptate commodo sit cupidatat non reprehenderit est. Pariatur et ipsum aliquip non et irure. Esse enim veniam deserunt magna anim ut quis elit dolore laborum est. Tempor in labore magna cupidatat sit labore cupidatat dolore.
