Michele Ceriotti
EPFL STI IMX COSMO
MXG 337 (Bâtiment MXG)
Station 12
1015 Lausanne
+41 21 693 29 39
Office: MXG 337
EPFL › STI › IMX › COSMO
Website: https://cosmo.epfl.ch/
+41 21 693 29 39
EPFL › STI › STI-SMX › SMX-ENS
+41 21 693 29 39
EPFL › STI › IMX › IMX-GE
+41 21 693 29 39
EPFL › STI › STI-DEC › STI-DIR
Mission
Awards
Volker Heine Young Investigator Award
2013
ERC Starting Grant
European Research Council
2016
IUPAP-C10 Young Scientist Prize
IUPAP
2018
ERC Consolidator Grant
European Research Council
2021
Fellow of the European Lab for Learning and Intelligent Systems (ELLIS)
ELLIS
2023
E. Bright Wilson Prize
Department of Chemistry, Harvard University
2024
Research
Current Research Fields
Teaching & PhD
Current Phd
Arslan Mazitov, Johannes Martin Spies, Markus Harald Fasching, Sandra Saade, Divya Suman, Joseph William Abbott, Egor Rumiantsev, Sofiia Chorna, Matthias Linus Kellner, Qianjun Xu, Wei Bin How, Filippo Bigi, Alessandro Forina
Past Phd As Director
Piero Gasparotto, Daniele Giofré, Bingqing Cheng, Edoardo Baldi, Venkat Kapil, Andrea Anelli, Félix Musil, Benjamin Aaron Helfrecht, Giulio Imbalzano, Andrea Grisafi, Dmitrii Maksimov, Chiheb Ben Mahmoud, Nataliya Lopanitsyna, Alexander Jan Goscinski, Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan, Sergey Pozdnyakov
Courses
Introduction to atomic-scale modeling
This course provides an introduction to the modeling of matter at the atomic scale, using interactive Jupyter notebooks to see several of the core concepts of materials science in action.
Lecture series on scientific machine learning
This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.
Statistical mechanics
This course presents an introduction to statistical mechanics geared towards materials scientists. The concepts of macroscopic thermodynamics will be related to a microscopic picture and a statistical interpretation. Lectures and exercises will be complemented with hands-on simulation projects.
Statistical methods in atomistic computer simulations
The course gives an overview of atomistic simulation methods, combining theoretical lectures and hands-on sessions. It covers the basics (molecular dynamics and monte carlo sampling) and also more advanced topics (accelerated sampling of rare events, and non-linear dimensionality reduction)