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--- workshops:mercury_epsc2021 [2021/09/01 11:03] – created admin
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 **Description** \\
 The work package "Machine Learning Solutions for Data Analysis and Exploitation in Planetary Science" within Europlanet 2024 Research Infrastructure will develop machine learning (ML) powered data analysis and exploitation tools optimized for planetary science.\\
-In this workshop, we will introduce an ML pipeline for the automated detection of boundary crossings around Mercury in solar wind time series data. First, we will briefly give an overview about the physical problem. Then, we will guide the participants through the developed ML code with the help of a sample data set of solar wind time series data from the MESSENGER spacecraft. At the end, we will also cover problems encountered during the development of the pipeline.
+In this workshop, held in the course of the[[https://www.epsc2021.eu/home.html|Europlanet Science Congress 2021]], we will introduce an ML pipeline for the automated detection of boundary crossings around Mercury in solar wind time series data. First, we will briefly give an overview about the physical problem. Then, we will guide the participants through the developed ML code with the help of a sample data set of solar wind time series data from the MESSENGER spacecraft. At the end, we will also cover problems encountered during the development of the pipeline.
 The code for the ML pipeline will be freely available on the repository "EPSC2021-MercuryBoundaries-workshop" of our [[https://github.com/epn-ml|public GitHub account]]. We strongly encourage the participants to clone the repository and have a look at the material prior to the workshop.
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 Thursday, Sept 16 2021, 12:45 - 14:15 CEST
-More details can be found [[https://meetingorganizer.copernicus.org/EPSC2021/session/41900|here]].
+More details and the meeting link can be found [[https://meetingorganizer.copernicus.org/EPSC2021/session/41900|here]].
-Europlanet 2024 RI has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871149.