2 min readfrom Machine Learning

PhD students in ML, how many hours on average do you work? [D]

Our take

PhD students in machine learning often juggle intensive research schedules, balancing focused work hours with necessary breaks for meetings and personal time. On average, many students work around 9 to 10 hours a day, strategically allocating their peak productivity times in the morning and evening. They utilize tools like Slurm for job scheduling to maximize efficiency, even as they navigate the complexities of coding agents that may create a false sense of productivity.

I generally work around 9–10 hours a day, but not contiguously. I can usually carve out a dedicated chunk of time in the morning, take lab or project meetings in the afternoon, and block out around 6–8 PM for commute, exercise, socializing, and dinner. I also get more work done in the evening, since my focus is often best then. On weekends, I mostly run errands and try out new food spots, but I also make sure to do at least a little bit of work every day.

I try to schedule my Slurm jobs so they run when I’m not actively working, so I can collect results when I get back. When I don’t have at least some Slurm jobs going, I feel anxious. I also feel pressure to use coding agents whenever I can. At the same time, I find that these agents can create an illusion of productivity: I end up with more “dead time” where I’m just waiting for the agent to finish thinking.

I’m in my 3rd year as a PhD student at a top-5 program for my field in the US, and I’ve been thinking a lot about time management recently. I'm done with classes and not TA'ing this quarter. I mainly target the 3 main ML conferences (though I would love to make every deadline consistently and don’t), plus core NLP venues and journals.

submitted by /u/akardashian
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