Meloncelli D1
1QCIF Digital Research, Brisbane, Australia
Biography:
Dr Danny Meloncelli is Head of Skills Development at QCIF, leading workforce development initiatives across the National Research Infrastructure community. Since joining QCIF in 2022, he has delivered training and consultation to support researcher capability building and community development. He holds a PhD in Chemical Sciences from the University of the Sunshine Coast, a Master of Data Science from UNSW, and is Carpentries-certified. As lead for the NCI training partnership, Danny coordinated the delivery of 27 workshops reaching researchers across Australia. His work focuses on designing scalable, impactful learning experiences that strengthen workforce readiness in emerging technology sectors.
Abstract:
As research becomes increasingly data-intensive, effective use of high-performance computing (HPC) and artificial intelligence systems is critical to research impact. However, a knowledge gap exists between national computational infrastructure and the broader research community. Researchers across disciplines require practical skills in programming, data analysis, machine learning and computational workflows to make effective use of infrastructure resources. This presentation describes outcomes from a national training partnership designed to address this capability gap.
The NCI-QCIF partnership delivered 27 workshops across foundational and applied computational topics, including Unix shell, version control with Git, Python programming, HPC fundamentals and machine learning methods. Workshops were delivered online to 1,548 registrants from 84 organisations, with 1,035 attendees across all sessions (67% attendance rate), and an additional 485 researchers on waitlists. Participants were predominantly researchers from universities (87%), particularly Higher Degree Research students (56%), reflecting the program's reach into the emerging researcher community.
Participant feedback was strong. Respondents reported a mean likelihood of 9.0 out of 10 for recommending QCIF workshops to colleagues. Eighty-nine per cent indicated workshops met or mostly met their expectations, and 90% indicated content difficulty was about right. Respondents particularly valued hands-on learning, clear instruction and practical materials that could be reused after workshops. Demand was particularly high for machine learning and AI topics, with waitlists exceeding 100 participants for several sessions.
This presentation offers evidence of the effectiveness of coordinated training programs in building researchers' capability and supporting the productive use of national research infrastructure.
