Dude your cv is great

You got two “performed” in the H2X lab item. Other than that it’s great

Thank you very much for that catch!!! I so missed it! :)

It looks a bit crammed, so I'd ditch the summary. I never read those anyway and if I can't tell at a glance what you've worked on, something is wrong. If you want to keep it, I would emphasize which sub branch of AI you're interested and/or specialized in.

I would emphasize that full-time industry experience: that'll tell me I won't need to show you how to work in a team and that mnist isn't the usual dataset quality you should expect. Do point out it's full-time. You can deduce it from the dates but I typically look at CVs for max 1 min for initial triage.

Otherwise it looks pretty great!

Good CV overall, I'd decrease the number of personal projects and increase the space allocated to experience. This is probably contrary to what you have heard, but you go for full time roles, you should put experience first and then education. I say this because you have good experience and you wouldn't want to join at entry level, but at least L4. For that, you should promote your experience.

I would suggest changing the publications to the standard format, nobody needs to know what journal/platform all of them are bad... Also drop PhD from the prof. name, they are assumed to have a Phd by default. Drop "selected" from selected projects.

Fabulous CV, well done! Am I reading it right though that you completed all of that work at Wobot in 3 days?

Thanks!

If you are talking about the date: 06/19 to 06/22, then that's in MM/YY format. So, 3 years.

Move experience above education since you have significant work experience. Similarly, move the team lead CV role to the top of that section, about the research assistant roles. Recruiters want to know you have work experience first and foremost. You come across as significantly less competent/senior to recruiters if the first thing they hear about is the stuff you're doing as a grad assistant.

Haha nice, I was looking at your latent disentanglement paper recently. Do you have any ideas for downstream tasks related to latent disentanglement? Not entirely sure where to apply my dissertation