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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:A general and efficient framework for atomistic machine learning
DTSTART:20210430T160000
DTSTAMP:20260506T203354Z
UID:5b67418d6fe3e0b0048536ca48225c01e909ac23749f422686ef6f19
CATEGORIES:Thesis defenses
DESCRIPTION:Félix Benedito Clément MUSIL\nThesis Director: Prof. M. Ceri
 otti\,\nMaterials Science and Engineering doctoral program\nThesis Nr. 79
 97\n\nTo take part in the public defense\, please contact directly the spe
 aker
LOCATION:Videoconference
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR