What the top 10% of PhD students do during their research time

Top-performing PhD students spend their research time turning “results” into publishable evidence—especially when experiments go wrong—rather than chasing a single, predetermined “outcome.” The key distinction is subtle but decisive: collecting results means treating unexpected failures as data. When an experiment derails, those students mine what happened for explanations, tables, graphs, and schematics that demonstrate what they learned. In one example, an electron beam dissolves a sample; instead of discarding the work, the failure becomes an analytical tool. In another, solar cells crystallize; rather than labeling the work as rubbish and moving on, the crystallization is leveraged for further experiments and additional visualizations. The result is a thesis packed with mistakes that were actively interrogated—because the real question becomes “why didn’t it work?” and “how can that failure generate new, defensible findings?” That iterative “why” process is presented as the engine behind a thick, publishable thesis.

That results-first approach also aligns with how supervisors often define success: papers in peer-reviewed journals that advance the supervisor’s career. To fit that reality, students typically need to produce publishable outputs and then translate those outputs into papers. There’s also an interpersonal component—some degree of responsiveness to what supervisors want, including regular communication and a willingness to give supervisors material they can champion. Yet the transcript argues that the strongest performers aren’t just strategic; they show genuine enthusiasm for the research. They don’t necessarily “live and breathe” the topic, but they are excited to discover what’s true, comfortable discussing their findings, and motivated by the subject itself. That intrinsic interest makes challenges easier to endure and helps students keep pushing through uncertainty.

Two practical habits then reinforce the motivation and the results pipeline. First is “turning up,” meaning consistent physical presence in the lab and consistent mental presence—being there, using the time, and building momentum. The transcript emphasizes that even small starts (like spending ten minutes on a task) can snowball into longer work sessions. Students who repeatedly skip lab time are described as being at higher risk of failure, not because effort is glamorous, but because absence breaks the feedback loop that produces results.

Second is speaking with supervisors regularly and using those meetings to adjust course when data contradicts the original plan. The transcript warns that both students and supervisors can become fixated on an idea, sometimes for ego or grant-commitment reasons. Top students use supervisor check-ins to challenge assumptions based on real-world results, asking whether the current path is still the right one and pushing for a shift when evidence demands it. In combination—failure-as-data, consistent presence, intrinsic curiosity, and frequent course-correction—these habits are presented as the practical recipe for reaching the top tier of PhD and master’s research performance.