Differences

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--- dlcp2023:start [22/04/2023 16:41] – [Organizers] stepanova
+++ dlcp2023:start [12/08/2023 22:46] (current) – [Important dates] admin
@@ Line 6: / Line 6: @@
 ====== The 7th International Conference on Deep Learning in Computational Physics ======
-**June 21-23, 2023 \\ SPbSU, St.-Petersburg, Peterhof, Russia**
+**June 21-23, 2023 \\ SPbSU, St.-Petersburg, Peterhof, Russia** {{  :dlcp2023:see_you.png?400|}}
 **Official website DLCP2023:** [[https://dlcp2023.sinp.msu.ru]]
@@ Line 27: / Line 27: @@
 [[http://www.sinp.msu.ru/en|{{:dlcp2023:sinp-logo.png}}]]
 [[https://english.spbu.ru|{{:dlcp2023:spbu-logo.png?110}}]]
-[[https://rcc.msu.ru/en|{{:dlcp2023:nivc-310x280.jpg?120}}]]
+[[https://rcc.msu.ru/en|{{:dlcp2023:rcc-logo.png?120}}]]
 [[https://lit.jinr.ru/en|{{:dlcp2023:lit-logo.png?120}}]]
 ===== The main topics =====
-**Track 1.** Machine Learning in Physics
+**Track 1.** Machine Learning in Fundamental Physics
   * Machine learning methods in particle astrophysics and high energy physics.
   * Fast event generators based on machine learning for simulation of physics phenomena.
@@ Line 62: / Line 62: @@
 ===== Important dates =====
   * Registration — until June 7, 2023
-  * Deadline for abstract submission — May 23, 2023
+  * Deadline for abstract submission — <del>May 23, 2023</del>  **June 1, 2023**
   * Notification of report acceptance — June 7, 2023
   * Conference dates - June 21-23, 2023
-  * Paper submission — August 14, 2023
+  * Paper submission — <del>August 14, 2023</del> -> August 21, 2023
 ===== Registration fee ======
@@ Line 129: / Line 129: @@
   * A. Demichev (SINP MSU, Moscow)
   * A. Kryukov (SINP MSU, Moscow)
-  * V. Rudnev, (SPbSU, SPb)
   * T. Trushina, (SPbSU, SPb)
   * M. Andreev, (SPbSU, SPb)
@@ Line 137: / Line 136: @@
 All correspondence should be addressed by e-mail: <dlcp2023@spbu.ru>.
+===== Sponsor =====
+[[https://rscgroup.ru|{{:dlcp2023:rsc-logo.png?140 }}]] Группа компаний [[https://rscgroup.ru/|РСК]] — ведущий российский и хорошо известный в мире разработчик и интегратор «полного цикла» инновационных энергоэффективных, высокоплотных, масштабируемых решений с жидкостным охлаждением для высокопроизводительных вычислений и систем для машинного/глубокого обучения (High-Performance Computing, Machine Learning/Deep Learning), центров обработки данных (ЦОД), Edge Computing решений и интеллектуальных систем хранения данных «по требованию» (storage-on-demand).