Astroparticles and Multimessengers
The community proposes a structured modular training strategy that includes:
- Short, focused courses (8 hours over two weeks) on data science, instrumentation, and specific topics like muography or space weather.
- Hands-on training via FabLabs and practical sessions in instrumentation.
- A two-semester s-level program: general overview in the first semester and specialised tools (e.g., simulations) in the second.
- Internships and intensive schools: 2-week or 3-month research internships, supported by collaboration with projects like LAGO.
- Flexible formats to accommodate students from different academic calendars and backgrounds, including advanced undergraduates.
- Inclusion of asynchronous learning and citizen science projects to broaden participation.
High-energy physics
- Creation of General Modular Courses: Develop a set of core modules covering foundational topics in theoretical and experimental HEP, detector physics and accelerator physics, and their applications (including medical physics).
- Tailored Learning Paths: Allow students to customize their coursework based on their thesis topic and research interest, ensuring targeted and relevant training.
- Delivery Format: Offer courses in hybrid formats (online and in-person) to maximize accessibility for students across different regions, including those with limited infrastructure.
- Thesis Co-Supervision: Promote co-advising of graduate theses across institutions within the EL-BONGÓ network, connecting students to mentors with diverse expertise.
- Seminars: these sessions feature invited experts in high energy physics and medical physics, providing students with exposure to cutting-edge research, real world applications, and current challenges in both fields.
Geophysics
The training framework follows a modular and adaptable model structured around the following pillars:
- Core Areas: Instrumentation, theoretical foundations, data science, and research internships.
- Hybrid Learning: Combination of online courses, asynchronous content, and in-person field schools.
- Summer Field Schools: Short-term intensive field training sessions focused on data collection and analysis.
- Curricular Integration: Embedding geophysics modules as electives or seminars in existing master’s programs.
- Collaborative Course Design: Institutions co-develop course content, adapt materials locally, and supervise joint research.
- Technical Capacity Building: Training in instrumentation, data acquisition, modelling, and AI for geoscience applications.
- Public Engagement: Inclusion of citizen science initiatives and hackathons to increase participation and societal relevance.
HPC and AI
The HPC-IA community’s training framework comprises:
- Four modular courses:
- Architectures of AI and computing.
- Federated and scalable AI models.
- Ethical considerations in AI.
- Deployment and validation of AI systems.
- Flexible program design: students can enrol in one or multiple modules depending on availability and interest.
- Target groups: master’s and PhD students and advanced undergraduates.
- Internships and exchanges: structured for the second and third years of the EL-BONGÓ project (2026–2027).
- Workshops and seminars: led by international and regional experts.
- Hackathons and citizen science initiatives: to promote engagement and practical application.
- Cross-disciplinary integration: linking computational training with the needs of other scientific domains