From the creation of four scientific communities, we designed specific training pathways in: High Energy Physics; Astroparticles and Multimessenger; Geophysics; Artificial Intelligence and Computational Physics; Artificial Intelligence. Each student chooses one of these areas to train and develop specific projects according to their research interests.

Pedagogical proposal

Training in Astroparticles and Multi-Messenger Physics

This community studies the astrophysics of cosmic rays, gamma rays, neutrinos, and dark matter. It also explores space weather phenomena and their effects on the magnetosphere, ionosphere, and ground level. The program employs detectors installed at several universities to teach students particle and astroparticle physics, particularly guiding them toward the measurement of muon decay.

The community proposes a structured modular training strategy that includes:

  • Short, focused courses (8 hours over two weeks) in data science, instrumentation, and specialized topics such as monographs or space meteorology.
  • Hands-on training through FabLabs and practical instrumentation sessions.
  • A two-semester intermediate-level program: an overview in the first semester and specialized tools (e.g., simulations) in the second.
  • Internships and intensive schools: research placements lasting 2 weeks or 3 months, supported by collaboration with projects such as LAGO.
  • Flexible formats to accommodate students from different academic calendars and academic backgrounds, including advanced undergraduate students.
  • Inclusion of asynchronous learning and citizen science projects to broaden participation.

Training in High Energy Physics

This community studies the most fundamental particles in nature and their interactions. To achieve this goal, we use scientific instruments that are unique prototypes worldwide, such as the Large Hadron Collider at CERN.

  • Creation of General Modular Courses: Development of a set of core modules covering fundamental topics in theoretical and experimental High Energy Physics (HEP), detector physics, accelerator physics, and their applications (including medical physics).
  • Tailored Learning Pathways: Students may customize their coursework according to their thesis topic and research interests, ensuring targeted and relevant training.
  • Delivery Format: Courses are offered in hybrid formats (online and in-person) to maximize accessibility for students from different regions, including those with limited infrastructure.
  • Co-Supervision of Theses: Promotion of joint graduate thesis supervision within EL-BONGÓ network institutions, connecting students with mentors of diverse expertise.
  • Seminars: Sessions featuring invited experts in high energy physics and medical physics, providing students with access to cutting-edge research, practical applications, and current challenges in both fields.

Training in Geophysics

This community seeks to understand the planet we inhabit and explain the phenomena observed on it. It applies the principles and methods of Physics to study the Earth and its phenomena at different scales, engaging in dialogue with Physics and other disciplines such as Geology, Chemistry, Electronics, Astrophysics, and Planetology.

The training framework follows a modular and adaptable model structured around the following pillars:

  • Core Areas: Instrumentation, theoretical foundations, data science, and research practice.
  • Hybrid Learning: Combination of online courses, asynchronous content, and in-person field workshops.
  • Summer School: Short, intensive field training sessions focused on data collection and analysis.
  • Curricular Integration: Integration of geophysics modules as electives or seminar courses within existing master’s programs.
  • Collaborative Course Design: Institutions jointly develop course content, adapt materials locally, and supervise collaborative research.
  • Strengthening Technical Capacities: Training in instrumentation, data acquisition, modeling, and AI for geoscientific applications.
  • Citizen Engagement: Inclusion of citizen science initiatives and hackathons to increase participation and social relevance.

Training in Artificial Intelligence and High-Performance Computing

This community integrates mathematical resources (algorithms), computational resources (platforms and infrastructure), and implementation mechanisms (applications) to address major challenges such as parallelism, sustainability, and physical coherence. It enables large-scale and real-time data processing, as well as high-volume computational simulations, including astrophysical trajectories and high-energy particle simulations.

The HPC–AI community training framework includes:

  • Four modular courses:
    • AI and Computing Architectures
    • Federated and Scalable AI Models
    • Ethical Considerations in AI
    • AI Systems Implementation and Validation
  • Flexible Program Design: Students may enroll in one or several modules depending on availability and interest.
  • Target Audience: Master’s, doctoral, and advanced undergraduate students.
  • Internships and Exchanges: Structured for the second and third years of the EL-BONGÓ project (2026–2027).
  • Workshops and Seminars: Delivered by international and regional experts.
  • Hackathons and Citizen Science Initiatives: To promote participation and practical application.
  • Interdisciplinary Integration: Linking computational training with the needs of other scientific fields.
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