My PhD Application Process and 1st Year PhD Student Experience Part 1

A structured, industry-facing PhD pathway can work even when a candidate doesn’t yet know their exact research topic—so long as the application process is built around fit, evidence, and training rather than a pre-selected project. In this account of a computer science PhD application and first-semester experience, the core turning point is a “center for research training” program in Dublin and two other universities, funded externally and designed to funnel students toward industry roles through internships, industry talks, and a highly scaffolded start.

Instead of the usual PhD route—where applicants often specify a precise topic and supervisor—the program emphasized the candidate’s background and readiness. The path began when a professor in a master’s course suggested computer science PhD study. After reaching out to a senior computer science figure at the university, the applicant was directed to the center’s program, which targets students from varied starting points, including math, statistics, and other non-traditional computer science backgrounds. The application submitted in March 2019 relied heavily on transcripts, any industry experience, relevant coursework, and a case for suitability, not on a fully formed research proposal. Two interviews followed in April–May, and acceptance came before a summer internship that served as a practical bridge into research.

That summer internship—four months in a university research group—was the first sustained, hands-on research project in computer science for the applicant. While the master’s thesis work sat in sociology and data analytics, the internship built confidence in coding, idea generation, and research execution with support from a supervising professor. It also clarified what a research rhythm might feel like before the PhD began.

The first semester then looked different from a typical PhD because the center’s training model is intentionally structured. Students start with a six-week boot camp split across multiple universities, designed to bring people from diverse backgrounds up to a shared level in statistics, machine learning, and research fundamentals. The boot camp also includes industry partner talks, a group project that mixes students across universities, and workshops focused on transferable skills—creative problem-solving, entrepreneurship, presentation skills for communicating jargon-heavy science to broader audiences, and career planning.

A distinctive feature was supervisor “matchmaking.” With no prior certainty about who supervisors would be, students participated in speed-dating style meetings with professors, followed by deeper one-on-ones and final selection. The applicant’s outcome was relatively rare within the cohort: strong interest in computer vision and related areas meant recommender systems stood out, and the applicant secured a supervisor aligned with machine learning plus a second supervisor bringing sports science domain expertise. Their research focus became applied recommender systems for marathon training.

Finally, the account ties productivity and motivation to the realities of early PhD uncertainty. After the boot camp (starting September 30 and ending mid-November), the applicant took a planned holiday and then spent December and early January reading heavily to understand the research area. That “not knowing exactly what to do” stretch—especially before the first proper supervisor meeting—helped motivate the launch of a YouTube channel focused on PhD progress and productivity, including time management strategies learned from productivity audiobooks and the challenge of adjusting from a full-time study/work mindset to a more flexible PhD schedule.