Ignacio Castiñeiras
Lecturer, Department of Computer Science, Munster Technological University
David Murphy
Lecturer, Department of Computer Science, Munster Technological University
Businesses are increasingly using AI to optimise their operations and remain competitive. That’s why there’s a huge demand for post-graduates with practical AI skills and experience.
AI applications are present in almost every field, including healthcare, energy, transportation, telecommunications, retail, financial, cybersecurity and advertising.
Essential AI talent is scarce
“The barrier to AI entry from a technological point of view is decreasing all the time,” explains David Murphy, Lecturer, Department of Computer Science, Munster Technological University (MTU). “So, going forward, all companies are going to find artificial intelligence impossible to ignore.”
Ignacio Castiñeiras, also a Lecturer in the Department of Computer Science at MTU, agrees with this assessment. “Businesses need to be able to gather and analyse as much data as they can in order to anticipate and react to future trends and challenges,” he says. “Everyone from SMEs to large corporations will need AI to optimise their resources and remain competitive.”
However, all have a significant hurdle to overcome: identifying people who are equipped with practical AI skills and experience. This isn’t easy because the demand for talent in this field is growing exponentially.
“There is a significant gap between the number of organisations demanding people with AI skills and the number of university leavers in the market,” admits Castiñeiras.
Everyone from SMEs to large corporations
will need AI to optimise their resources
and remain competitive.
Hands-on approach equips students with practical skills
To help address this problem, MTU has developed a 100% continuous assessment Master of Science degree in artificial intelligence — taught by AI experts — providing students with an in-depth understanding of modern AI techniques. These mirror how AI is typically leveraged in industry and range from natural language processing and deep learning to machine vision and robotics.
Students are expected to have an established competency in computer programming and can complete the programme full-time on campus over a year or part-time, online, over two years. All should prepare for an emphasis on learning by doing, with practical/programming-based assessments required for each module. Castiñeiras says: “Everything the students learn in theory, they put into practice with assignments that are based on real-world problems. For me, that hands-on approach is crucial.”
Diverse career paths post-graduation
Graduates have gone on to work in a diverse range of roles, including data science, natural language processing, data analysis and autonomous driving engineering and some begin PhDs. “Both cohorts — full-time and online — find the course challenging,” says Murphy. “They also appreciate that challenge, whether they are online students who are already in work and looking to upskill or full-time students looking for a new career path.”
