I receive a lot of enquiries for Master/PhD/Postdocs (depending on the period, from 1–2 to 10 per week) and I do not answer all of them. In general I look at an email for ~1 minute while on the metro, and look for information to decide whether it’s worth getting back to you. If there’s no CV, transcript of records or publication list I can’t gauge whether we’re a match, so I will likely ignore or forget your email.
For spontaneous applications, I have limited time, and due to the volume of enquiries I get I rather ignore them than get back to you unless you have an exceptional profile or I happen to have a lot of time available. If you don’t hear back from me after
If you follow the information below, however, it’s highly likely that i will notice and acknowledge the effort you took in writing that email and will get back to you.
What your email should contain
To maximise the chances that I get back to you, your email should clearly contain at the very least the following:
- Who you are, where you come from academically, what you studied/worked on, what your interests are, and what path you want to take in the future;
- How my research interests match yours. If you’re changing field (mainly relevant for postdocs), show me you understand what you’re getting yourself into and why;
- If you are looking for a postdoc: a list of publications/link to Scholar and a brief comment on the ones most relevant to your application;
- If you are looking for an internship/PhD: your transcript of records for the last few years. Do not worry about perfect scores (I was not the best student of my year), but I appreciate a brief comment on your strengths/weaknesses and how you think they will play out if you join us;
Also good to include:
- Did you work with someone in the past, especially someone I might know? Mention it;
- Do you have code on GitHub? Show me — it will play in your favour;
- Did you read some of my papers and think they connect to something you’ve been working on? Let me know what you’re thinking. And don’t use an LLM for this — it’s easy to tell when one is writing in your place. I want to know how you think, not how ChatGPT does;
- For more senior people: I don’t expect you to know exactly what i do, but if you come from a different field i would love to hear about why it’s a good idea to combine your background with our expertise;
Some don’ts:
- If your email clearly sounds LLM-generated, it will be ignored. I use LLMs myself and they are great, but if I get the feeling you spent less time writing your email than it takes me to read it, I won’t dedicate brain bandwith to it;
- Especially for Master students and PhD applicants: don’t pick a random paper of mine and tell me “it’s super interesting”. Show me you understand it and connect it to your own interests;
Topics I’m interested in and profiles I consider
The bulk of my research is Machine Learning applied to Many-Body Quantum Physics. I enjoy both methodological development and the application of existing methods to open problems.
Topic-wise, my current interests from an applications standpoint revolve around high-precision quantum chemistry, generation of data for interatomic potentials, superconductivity in Hubbard-like electronic systems, frustrated magnetism, and out-of-equilibrium systems with quenches.
Methodologically, I am interested in foundational quantum-state methods that can target the whole phase diagram, surrogate models, methods targeting excited states and finite-temperature states, and my long-standing challenge of simulating dynamics (a very hard one). I am also interested in hybrid tensor network–Monte Carlo methods.
I did work in the past on variational quantum algorithms, nowadays known as “quantum machine learning”, but if that is what you want to work on — sorry, I will probably not even reply. It does not excite me much anymore. I am interested in hybrid classical–quantum algorithms that combine variational Monte Carlo with quantum subroutines, but that is a side quest for me.