{"id":1512,"date":"2025-01-02T21:31:00","date_gmt":"2025-01-02T21:31:00","guid":{"rendered":"https:\/\/leap.physics.auth.gr\/?p=1512"},"modified":"2025-01-03T21:45:55","modified_gmt":"2025-01-03T21:45:55","slug":"ccs24-leaps-impactful-close-and-a-look-ahead-to-2025","status":"publish","type":"post","link":"https:\/\/leap.physics.auth.gr\/?p=1512","title":{"rendered":"CCS’24: LEAP’s Impactful Close and a Look Ahead to 2025"},"content":{"rendered":"\n

As 2024 has just closed, we take a moment to reflect on the milestones and achievements that marked this year for LEAP. One of the standout moments was our participation in CCS’24<\/a> this past September, a pivotal event sponsored by the Complex Systems Society (CSS)<\/a>. CSS emphasises the role of Complex Systems science in addressing critical societal challenges and enhancing the connection between scientific inquiry and societal needs. It provides a framework that fosters multidisciplinarity and innovation, actively working to bridge gaps between natural and social sciences, engineering, and policy, thus correlating tightly with our mission at LEAP – to reduce the gap between academic education and real-world market needs. Regarded as the most important annual meeting for the complex systems research community, the CCS conferences bring together academics, researchers, and innovators from diverse scientific fields.<\/p>\n\n\n\n

Maria Tsouchnika<\/a> and Michael Kanetidis<\/a> from Aristotle University of Thessaloniki (AUTh)<\/a>, and Cigdem Yalcin<\/a> from Istanbul University (IU)<\/a>, had the opportunity to showcase LEAP and its results, exchange ideas and insights with peers, and expand LEAP\u2019s network at CCS’24, jointly organised by Northeastern University London<\/a> and the University of Exeter<\/a> in September. Our presentation revealed insights from our recent study, which explores critical questions regarding the educational needs of the tech industry, such as whether there are notable differences in the difficulty of recruiting for Machine Learning versus Data Analytics roles, how gender influences perceived capabilities among individuals pursuing tech roles, and whether there are disparities in foundational knowledge across critical fields such as Complex Systems, Machine Learning, and Data Analytics, among others. These inquiries are vital as we strive to understand the underlying factors that shape the landscape of tech employment and education.<\/p>\n\n\n\n