随着天文学研究迈入大数据时代，海量数据的处理与分析已成为科研工作的核心挑战。为应对这一趋势，金砖国家天文学工作组（BRICS Astronomy）正式推出“数据科学与人工智能培训项目”（Data Analytics & AI Training Programme）。该项目是一个为期10周的线上培训计划，旨在面向从初学者到资深研究者不同层次学员普及数据科学与机器学习的前沿方法，并将其深度融入天文学实践。课程内容涵盖数据科学与机器学习的核心领域，通过大量天文实际案例的动手操作，帮助学员掌握处理、分析与解读大型天文数据集的关键技能，进而探索在金砖国家框架下开展创新研究的新路径。项目的实施将有力提升金砖国家在天文数据分析领域的能力建设，推动区域内科研合作的深化与发展。
该项目于2026年3月5日至31日开放申请，并拟于2026年5月4日至6月10日期间举办培训。如您有兴趣申请，可以查看下方通知原文，了解更多详情。
Call for Application: Data Analytics & AI Training Programme
Training Program
The BRICS Astronomy is excited to announce an opportunity for participants to engage in an innovative training course that blends data science and machine learning (ML) with practical applications to Astronomy. This 10-week virtual program aims to empower individuals at all skill levels, from beginners to advanced users, to deepen their understanding of data science concepts and enhance their research capabilities. The training program will provide hands-on learning experiences across core areas of data science and machine learning, emphasising real-world applications in Astronomy.
Throughout the course, participants will be guided in using these tools to manage, analyse, and interpret large datasets, while also exploring new possibilities for innovative research within the BRICS countries.
Key Objectives
- Enhance Skills: Equip participants with the knowledge and practical tools that will allow them to effectively manage and analyse scientific data.
- Foster Innovation: Support the development of applications in machine learningfor use in astronomy and related scientific fields.
- Build Community: Create a collaborative space for participants to share ideas, challenges, and solutions, strengthening the broader research network.
Who Should Apply?
This program is designed for early-career professionals, university students, and individuals working within the field of Astronomy who are looking to upskill themselves,particularly those from BRICS member countries. If you are eager to enhance your datascience skills in fields like Astronomy or other STEM disciplines, this program is foryou. This program covers key topics such as Python programming, data structures, control flow, data analysis, and handling astronomical data. You’ll also explore statisticaland time series analysis, and machine learning basics for astronomy, and complete acapstone project to showcase your skills.
What's Required?
- Basic Knowledge: No prior experience is required for beginners. However,participants should be interested in Python programming, data science, and astronomy.
- Tools: Access to a working personal computer and internet access to participate in the virtual training fully.
- Commitment: The participant should commit to attending all virtual sessions during the 10-week course. The participants will also be required to work on the course material in their own time and participate in online discussions.
Training Structure
The virtual program will span over 10 weeks, with a new topic covered each week. Each week’s session will include:
- Workshopping: Focused on core data science and programming skills.
- Hands-on Coding: Practical learning of data science tools and techniques.
- Interactive Q&A Discussions: Opportunities to ask questions and gain deeper insights into the training material.
- Resources: A week before each session, participants will be given the training material based on the week’s topic, including various problem sets and resources to work on in preparation for the week’s session.
- Tutors: Tutors will be available weekly to support participants and help with problem-solving.
Project Timeline
- Applications open: 05 March 2026
- Application Deadline: 31 March 2026
- Approximate Program Start Date: 04 May 2026
- Approximate Program End Date: 10 July 2026
Application Process
- Apply Online: Complete the online application form.
- Online Form：https://wkf.ms/4bmNt4y（点击文末“阅读原文”可直达）
- Selection Criteria: Applicants will be selected based on interests, relevantexperience, and alignment with the program’s objectives.
Selection Framework
- Alignment of Interests
- Criteria: Applicants should demonstrate a genuine interest in learning more about data science, machine learning (ML), AI, and their applications in astronomy or related scientific fields.
- Evaluation: Candidates will be assessed on how their personal or professional interests align with the objectives of the program, including how they plan to apply the skills gained in their field of study or work.
- Willingness to Contribute Collaboratively
- Criteria: Applicants must show a commitment to working collaboratively and actively contributing to the program’s community.
- Evaluation: The ability to engage in group discussions, share ideas, and work effectively with others will be assessed through personal statements and past collaborative experiences (e.g., team projects, research work, or community involvement).
- Availability and Commitment
- Criteria: Participants must be available to commit to all 8 weeks of the course, as well as engage with the program’s workshops, coding sessions, pre-session training material, and discussions.
- Evaluation: Candidates will be assessed based on their stated availability and ability to balance program participation with other responsibilities.Preference will be given to those who can demonstrate consistent time commitment.
Communication Tools
- Slack: For ongoing communication and community-building.
- Google Workspace: For document sharing and collaborative work.
Support & Resources
- Learning Materials: Participants will receive tutorials, coding notebooks, datasets, and other resources tailored to the specific needs of BRICS.
- Tutors: Tutors will be available weekly to support participants and help with problem-solving.
Tutors and Support
Experienced tutors will be available to assist participants throughout the learning process. They will offer guidance to ensure a smooth and productive experience.
Evaluation
The program’s success will be evaluated through continuous feedback from both mentors and mentees, allowing for adjustments to ensure it meets the needs of participants and achieves the intended outcomes.