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.