About us

From self-driving cars to chess playing computers, from autonomous booking agents to automated financial trading systems, applications of Artificial Intelligence (AI) are having massive impacts in ever growing aspects of our lives. The main driver of this growth has been the Machine Learning (ML) technologies that underpin these high-profile AI successes. For the first time, the availability of large data sets needed to train sophisticated ML solutions is matched by the computational power to learn from this data. The result has been an explosion in the successful application of ML technologies to real-world problems in a variety of industries, from advertising and commerce to agriculture and healthcare, and from transport to manufacturing.

The Science Foundation Ireland Centre for Research Training in Machine Learning is designed to address the urgent industry demand for ML talent. The centre trains academically outstanding, industry-ready PhD graduates in tightly connected cohorts. These graduates will be future leaders managing the disruption that ML is causing across industry and society, and will strengthen the reputation of Ireland as a global hub for ML education, research, and application.

ML Fundamentals

The fundamental theory, algorithms, techniques, and technologies on which ML is based.

ML in Society

From the displacement of jobs to the creation of filter bubbles, ML is having an enormously transformative effect on society which needs to be examined, understood, addressed, and communicated.

ML Practice

As ML technologies have moved out of the lab, a body of best practice has emerged around how to design, develop, deploy, and maintain ML solutions; as well as how to organise the teams that do this work and the projects that they do.

ML Applications

ML is having a disruptive effect on industries from fashion to agriculture which is driving new ways of operating in these industries and new ML approaches to match industry-specific demands.

PhD Training Programme

ML-Labs delivers an ambitious programme of study that allows students to develop the deep research and technical expertise required for an internationally excellent PhD, alongside the transferable skills and industry experience to make them attractive  industry-ready hires. The programme builds upon the excellent structures and activities that already exist within the three host institutions, and adds innovative aspects specifically tailored to the goals of ML-Labs. The key training components are outlined below.

Bootcamp

The first 6 weeks of a student’s experience at ML-Labs will be a mandatory, full-time, cohort-based programme of activities. These will include short, focused training courses in machine learning, research methods, research ethics, data protection and other core topics; an experiential learning team-based facilitated project in which students will build an end-to-end machine learning solution; peer-led knowledge sharing; and workshops with industry partners on applications of machine learning.

Student-Supervisor-Project Matchmaking

Students will join the ML-Labs programme without having made a commitment to work on a specific research project with a particular project supervisor. During the initial Bootcamp a series of matchmaking will be organised to allow students and supervisors to meet to discuss research interests and to find the best matches between students, supervisors, and research topics. This process has been designed this way to allow all involved have plenty of opportunities to get to know each other and as it has been shown to work very well on internati0onal PhD training programmes.

Summer Schools

An annual week long Summer School will be a central event in the ML-Labs academic year. This event will be organized by the students themselves, through an annually refreshed Organising Committee, with representation from across all host institutions. The Summer School will bring together all ML-Labs cohorts and staff to encourage teamwork and scholarly exchange.

Taught Modules

The programme requires students to complete a minimum of 30 ECTS taught credits, with 15 ECTS of core mandatory modules (in maths and statistical inference, deep learning and big data programming) to be completed within the first 18 months of the programme. 15 ECTS will be taken as elective modules and will be chosen by the student in collaboration with their supervisory team and with reference to each student’s career plan.

Placement

All ML-Labs students will undertake a placement in an enterprise, other non-academic establishment, or an international research group. Placement will typically be in a single block of 3 to 6 months in duration, although other options will be available if required.