Sustainable IT - sustAIn.brussels training powered by ULB & VUB expertise
Implementing digital technologies sustainably and responsibly
Course overview
The digital transformation and sustainability aspirations of companies are intertwined, presenting a key challenge. Our Sustainable IT course introduces business professionals to a nuanced, technology-oriented, and pragmatic view of implementing digital technologies sustainably. Explore expert perspectives on cybersecurity, responsible IT practices, and the intricate interplay between digital technologies and environmental/social sustainability.
Advanced/technical track
Course dates
- Only the basic course (Course 1 - 6h) - 22 May 2024
- Basic course (Course day 1 - 6h) + Advanced course (Course day 2 - 3h) + workshop (Course day 3 - 3h) - 22, 24 May & 5 June 2024
Course content
- Introduction to the interlinkage between digitalization and sustainability
- Linking sustainable IT with broader corporate sustainability and reporting
- Measuring is knowing: tools to measure sustainability impacts of IT
- Use cases and business-oriented applications of concepts
Learning outcomes
- Pragmatic, technology-oriented view on sustainable digitalization in corporate environments
- Intuition for linking digital technologies with their sustainability impacts
- Knowledge of tools for measuring and reporting impacts
- Insights into best practices from the field
Meet our expert instructors
Julien Gossé
Researcher, PhD candidate, and lecturer at Solvay Brussels School of Economics and Management, ULB.
Specializes in exploring the intersection of digitalization and sustainability.
Actively involved in developing AI strategy for Brussels Region at FARI.
Arjen Van de Walle
PhD candidate at Vrije Universiteit Brussel, supporting sustainable digital transformation.
Background in bio-science engineering and sustainability science.
Expertise in sustainable business model innovations and coaching entrepreneurs.
Dimitris Sacharidis:
Assistant professor at Université Libre de Bruxelles, focusing on responsible data science and AI.
Research interests include mitigating bias in data and ML models, ensuring transparency in complex pipelines and black-box models, and optimizing data-intensive processing pipelines.