Advanced Schools in the frame of PRP@CERIC
The PRP@CERIC project involves the organization of a series of Advanced Schools at the partner locations.
These schools are primarily intended for researchers and technologists employed within the project and to a lesser extent for external students, interested in progressing their career by becoming members of the scientific staff of a Research Infrastructure (RI) or in improving their basic knowledge as RI users belonging to the academic or industrial world.
The schools’ ambition is to provide students with theoretical scientific training and more specialized technical-practical skills on the pillars of the project, thus contributing to promoting the long-term sustainability of the PRP@CERIC ecosystem in terms of both user base and operation.
Below is a list of all the Advanced Schools, associated with the respective organizers and completed with relevant details.
Master in Data Management and Curation
The one-year professional course “Master in Data Management and Curation (MDMC)”, a unique joint initiative by Area Science Park and SISSA, is designed to equip the next generation of data professionals with the advanced, practical skills needed to excel in this critical field.
Master in Data Management and Curation (MDMC)
Pilot training course in data management and curation, organized by Area Science Park, SISSA, and CNR-IOM.
Mass Spectrometry-Based Multi-Omics Principles and Applications in Life Sciences
The goal of the school, organized by the University of Salerno, is to provide a comprehensive overview of the theoretical foundations of mass spectrometry and its applications in multi-omics fields such as metabolomics, lipidomics, and proteomics, with a particular focus on state-of-the-art methodologies.
Exploiting HPC Infrastructure for Training Large AI Models
The school was organized by the University of Salerno and aimed at providing students with an update on the most recent scientific and technological advancements in the adoption of AI models in healthcare. It also offered knowledge and practical skills for training and deploying advanced AI and deep learning models, including emerging large language models (LLMs), for the analysis of image and omics data on large HPC datacenters.