Cold emailing profs about PhD positions? Read this [D]
Our take
The annual surge in PhD application emails is a familiar frustration for academics, and the recent Reddit post from /u/tariban offers a particularly insightful, if pointed, guide to navigating this process effectively. It’s a stark reminder that even in an increasingly AI-driven world, genuine human connection and thoughtful engagement remain paramount. The core message resonates: a generic, mass-produced email has a vanishingly small chance of success. This isn't merely about crafting a polite message; it's about demonstrating a clear understanding of a potential supervisor’s work and a genuine interest in their specific research direction. We’ve previously explored how even seemingly perfect LLM outputs can be fundamentally flawed Your LLM Can Return Perfect JSON and Still Be Wrong, highlighting the dangers of relying too heavily on automated tools for critical thinking. Similarly, the observation that summarising a professor’s paper is a wasted effort underscores the importance of demonstrating independent thought and the ability to build upon existing research.
The emphasis on avoiding generic research interests – “Machine Learning, LLMs, and AI” – is particularly astute. It reflects a broader trend of superficial engagement with complex fields, fueled in part by the ease of access to introductory materials. Prospective students need to demonstrate a depth of understanding that goes beyond buzzwords and a clear vision for how their interests align with a specific research group. This connects to the larger discussion around AI's impact on expertise, as detailed in our piece about Apple’s new Mac line Apple's New Mac Line is Built Around Local AI. The Bet Is You'd Rather Own Than Rent. The desire for ownership and control, whether of data processing or intellectual contribution, is a powerful driver, and the /u/tariban post reinforces that principle – a PhD application isn’t about outsourcing your thinking; it’s about showcasing your intellectual independence. Furthermore, the warning against excessive AI use echoes concerns about how AI might inadvertently hamper innovation, as discussed in our analysis of AI’s potential to disrupt government hacking tools How AI could make it harder for governments to use hacking tools.
The post's practical advice – checking websites for specific instructions, avoiding lengthy emails, and respecting the professor’s time – are timeless principles of effective communication, amplified in the context of academic recruitment. The increasing volume of applications means that supervisors are forced to triage quickly, and anything that signals a lack of effort or attention to detail will likely be filtered out. It’s a sobering reminder that the pursuit of advanced education requires more than just talent; it demands diligence, respect, and a willingness to engage authentically with the academic community. The reliance on LLMs to generate boilerplate applications, while seemingly efficient, ultimately undermines the very purpose of the exercise – to demonstrate one's potential as a future researcher.
Ultimately, the /u/tariban post serves as a valuable corrective to the increasingly automated and superficial approaches to academic pursuits. It reinforces the importance of genuine intellectual curiosity, careful research, and respectful communication. As AI continues to reshape how we work and learn, it’s crucial to remember that human connection, critical thinking, and a demonstrated passion for one's chosen field remain the most valuable assets in any endeavor, especially the pursuit of advanced knowledge. The question now becomes: how will future generations of students learn to navigate this evolving landscape, balancing the benefits of AI with the enduring need for authentic intellectual engagement?
This is the time of year when the number of cold emails I receive about PhD positions tends to ramp up quite a bit. In many countries, this cold emailing is essentially part of the normal recruitment process, so there is nothing inherently wrong with doing this. However, there are a few things you definitely shouldn't be doing:
- Massive emails. The probability of me reading your email is inversely proportional to its length.
- Emailing everyone. Find supervisors that work in areas you are actually interested in. I do relatively foundational ML research (i.e., not associated with a specific application domain), but the majority of emails I get from prospective students are essentially "I want to apply ML to domain X". In many cases this does not constitute an ML research direction; you'd be better off finding a supervisor with expertise in domain X, which is where most of the impact will be.
- Generic research interests. If the most specific research interests you can give are "Machine Learning, LLMs, and AI" then I assume you only have a surface-level familiarity with the field, and are not ready for a PhD.
- Passing off workshop papers as conference papers. This has become a much more common thing in the last couple of years. It's a big red flag; I am not going to take on someone who is dishonest.
- Excessive AI use. Using them for fixing up grammar is fine, but if you outsource your thinking to LLMs then your research direction will be the same as everyone else who outsources their thinking to LLMs. This tends to result in something that would be an okay bachelor's thesis project, but nothing more than that. I get a lot of LLM emails, so determining if you are in this cluster is very easy.
- Summarise my paper. I already know what's in it, I don't need a summary. I care more about how you think you could build on it, or do something related. Don't use LLMs for this; see above point.
- Ignoring instructions on my website. Check prospective supervisors' websites for how you should be getting in contact with them. Often they will ask you to include something in the subject line to make sure your email goes to the right place. Ignoring this will send you straight to spam.
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