Like the rest of the world, Ireland is preparing for a future driven by new technologies. A critical aspect of this is finding people with the right skills to push boundaries and give businesses a competitive edge.
Mastercard’s Dublin tech hub is hoping to attract these people through its partnership with ML-Labs, which is a collaboration between University College Dublin (UCD), Dublin City University and TU Dublin funded by the Science Foundation Ireland Centres for Research Training programme.
Together, they are hosting a new PhD internship to help students from ML-Labs’ machine learning programme find their feet in industry. And we spoke to Mastercard’s Dr Steve Flinter, UCD’s Dr Brian Mac Namee and PhD candidate Sagar Saxena to learn more.
They described the PhD internship as a unique and mutually beneficial programme for both Mastercard and ML-Labs. “This programme is going to give us access to top-grade PhD candidate students,” explained Flinter, who is vice-president of AI and machine learning at Mastercard Labs.
“Our aim for the programme is that we can support the training of those PhD students to help them get to a point where they can translate their training and their education into industry once they’ve graduated.”
Through the internship, PhD students work with one of the teams at Mastercard Labs for four months. Saxena is the first Mastercard intern on the programme and he’s interested in what Flinter described as a “very topical area”, but one that Mastercard hadn’t researched before – explainable AI for time series.
Speaking about his time on the PhD internship programme so far, Saxena said that the team had been supportive. “All in all, working in Mastercard seems to be a game changer and I think the experience that I’ve accrued here would help me shape my future for the better.”
Mac Namee added that the internship programme can help PhD candidates who want to move from academia into industry. “It’s designed to be accessible to applicants from all kinds of different backgrounds,” he said.
“So whether you’re interested in working on new and exciting machine learning algorithms or bringing machine learning into a new area, or even looking at the societal impacts of machine learning, there’s a place for you.”
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