Philippe Schwaller

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philippe.schwaller@epfl.ch +41 21 693 20 56 https://schwallergroup.github.io

EPFL SB ISIC LIAC
CH J2 492 (Bâtiment CH)
Station 6
1015 Lausanne

+41 21 693 20 56
Office:  CH J2 492
EPFL > SB > ISIC > LIAC

Web site:  Web site:  https://www.epfl.ch/labs/liac/

EPFL SB ISIC-GE
CH j2 492 (Bâtiment CH)
Station 6
1015 Lausanne

+41 21 693 20 56
Office:  CH J2 492
EPFL > SB > SB-SCGC > SCGC-ENS

Web site:  Web site:  https://www.epfl.ch/schools/sb/scgc/

EPFL SB ISIC-GE
CH j2 492 (Bâtiment CH)
Station 6
1015 Lausanne

+41 21 693 20 56
Office:  CH J2 492
EPFL > VPA-AVP-PGE > AVP-PGE-EDOC > EDCH-ENS

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Administrative data

Teaching & PhD

Teaching

Chemistry and Chemical Engineering

PhD Students

Agostini Sandro, Armstrong Daniel Paul, Chen Junwu, Guo Jeff, Kopf Sarina Nicole, Marulanda Bran Andres Camilo, Neeser Rebecca Manuela, Raffaud Sacha Mark Yann, Rankovic Bojana, Sabanza Gil Víctor, Schilter Oliver Tobias, Sin Wing Pong, Valverde Das Neves Paulo Filipe,

Courses

Practical programming in Chemistry

(Coursebook not yet approved by the section)

AI for chemistry

(Coursebook not yet approved by the section)

AI in chemistry and beyond:Highlights in the field

Should have expertise in chemistry, physics or lite and material sciences. Although a very good knowledge in Al-based algorithms is required to fully understand the technical details, a basic knowledge is sufficient to understand the potential of these methods and their applications

AI in chemistry and beyond: Trends in the field

(Coursebook not yet approved by the section)

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.