SFT 2019 - Lectures on Statistical Field TheoriesFeb, 04 2019 - Feb, 15 2019
Apply (deadline: Nov, 15 2018 )
Note: you are applying after the deadline
Denis Bernard (ENS, Paris)
Pasquale Calabrese (SISSA, Trieste)
Andrea Cappelli (INFN, Florence)
Filippo Colomo (INFN, Florence)
Fabian Essler (University of Oxford)
Giuseppe Mussardo (SISSA, Trieste)
The aim of the school is to bring together PhD students with interests in low-dimensional quantum field theory, conformal field theory and integrable models, and their applications to statistical mechanics and condensed matter systems, and to help them building a solid and specialized background on these subjects. The school provides sets of postgraduate lectures covering introductory topics as well as recent developments in the field.
Lectures will be scheduled four hours each morning, for a total amount of about forty hours, over two weeks. Presentations will be given on the blackboard. The afternoon will be devoted to exercises, study, and discussions with lecturers and senior participants. A desk and standard research facilities will be provided to all participants.
The school can admit up to forty participants. Accomodation for two weeks in twin rooms will be provided for a total price of about 400 €. Financial support to accomodation expenses may be provided, upon request, to at most thirty participants.
The courses will be officially part of the Italian Ph.D. training program for the universities that have joined the initiative. For this purpose there will be the possibility of a final exam with the lecturers.
Bruno Bertini (University of Ljubljana): Transport in closed one-dimensional systems
Jérôme Dubail (Université de Lorraine, Nancy): CFT curved-space approach to inhomogeneous systems
Thierry Giamarchi (Université de Genève): Tomonaga-Luttinger liquids: from field theory to experimental realisations
Giuseppe Santoro (SISSA, Trieste): Floquet physics and applications to non-equilibrium quantum systems
Frank Verstraete (University of Gent): Statistical Physics in the language of tensor networks