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SUMMARY:Biophysically accurate and machine learning-based surrogate models
  to optimize neuroprosthesis design and operation
DTSTART:20240617T160000
DTSTAMP:20260316T091929Z
UID:8c206ee6b5c4eefd1a59c0c71838bf2cd0c21b086d93850f6a7d6b55
CATEGORIES:Thesis defenses
DESCRIPTION:Simone ROMENI\nThesis Director: Prof. S. Micera\,\nElectrical 
 Engineering doctoral program\nThesis Nr. 10622\n\nTo take part in the pub
 lic defense\, please contact directly the speaker
LOCATION:Campus Biotech Bâtiment H8  Chemin des Mines 9  1202 Genève htt
 ps://epfl.zoom.us/j/63936409897?pwd=MrcEbtwDEwoonYHf2FppRYgo2CFXFm.1
STATUS:CONFIRMED
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