Do you want to be at the forefront of technological innovation and interdisciplinary collaboration at esade? enode is your platform to explore, learn and contribute to real-world solutions in these fields.

Membership Opportunities

General Members

Who: Open to all students, regardless of technical skills.

What: Participate in non-technical roles such as marketing, logistics, management, or event planning. Opportunities include joining the executive board, leading a department, or supporting projects and Impact Initiatives with knowledge in business, finance, and more.

Why: Contribute to enode’s mission while gaining valuable insights into AI, ML, and Data Science from a non-technical perspective. Enhance leadership skills and support initiatives bridging the gap between tech and business.

Technical Members

Who: Ideal for students with a strong interest/background in programming, data-driven algorithms and AI technologies.

What: Take the lead in technical aspects of Innovative Labs and Impact Initatives. Engage in hands-on coding projects, solve real-world challenges, and collaborate with peers and industry experts on cutting-edge topics.

Why: Apply and expand your technical skills in impactful projects, guided by experienced mentors. Work alongside like-minded individuals while building your professional portfolio and gaining exposure to advanced AI and data-driven applications.

A note for prospective technical members

Prospective technical members might go through a technical interview to assess skills and interests, depending on the amount of technical member applications received. All potential technical interviews are are designed to gauge mainly foundational knowledge, ensuring inclusivity while maintaining standards. They key areas to assess include:

  • Programming Fundamentals: Language proficiency in Python. Variables, data types, control structures, basic data structures (arrays, lists, dictionaries, etc).

  • Data Manipulation: Understanding of data handling using libraries like Pandas or Numpy. Loading datasets, basic data cleaning (missing value handling, data filtering), data transformations.

  • Foundational Machine Learning Concepts: Familiarity with basic ML concepts. Proficiency or knowledge in common frameworks such as TensorFlow, Torch, Keras or Sci-kit Learn are a big big plus.