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
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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
PhD Students
Joseph William Abbott, Filippo Bigi, Sofiia Chorna, Anne Sofie Darket, Markus Harald Fasching, Alessandro Forina, Wei Bin How, Matthias Linus Kellner, Arslan Mazitov, Egor Rumiantsev, Sandra Saade, Johannes Spies, Qianjun Xu
Past EPFL PhD Students
Andrea Anelli (2020), Edoardo Baldi (2020), Chiheb Ben Mahmoud (2023), Bingqing Cheng (2019), Piero Gasparotto (2018), Daniele Giofré (2018), Alexander Jan Goscinski (2024), Andrea Grisafi (2021), Benjamin Aaron Helfrecht (2021), Giulio Imbalzano (2021), Venkat Kapil (2020), Nataliya Lopanitsyna (2023), Dmitrii Maksimov (2022), Félix Musil (2021), Jigyasa Nigam (2024), Sergey Pozdnyakov (2025)
Courses
Introduction to atomic-scale modeling
MSE-305
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
PHYS-754
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
MSE-421
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
MSE-639
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)