Applying for PhD fellowships but unsure where to start? This is what I’ve learned from talking with previous recipients, professors, industry insiders, and first-hand experience with 8 applications, 5 rejections, and 3 awards (IBM, NSF, and Raytheon) during my machine learning PhD.
Graduating with your PhD is euphoric…after all, you’re finally free! This is your shot to make all those sleepless nights worth something and
cash out on all that sweat equity you labored over for the past few years.
But before you go and pop the celebratory champagne, you have to navigate the ML job market—the technological equivalent of the 19th century Wild West—where
every company, hiring manager, interviewer, and intern has a unique interview process and wildly diverging criteria for what makes a good candidate.
So what does this mean for newly minted ML/CS PhD students? Well, it means you have a long journey ahead. Fortunately, you won’t go it alone. This post
is divided into two sections sharing every tip and trick I learned from 104 interviews across 30 companies and 38 teams.
For many aspiring ML PhD students the application process is shrouded in mystery, feeling like
a high stakes interview where you have no idea how you are evaluated.
When I applied to PhD programs in 2017, I had a million questions. How do I select a school? What’s the best way to find an advisor? Who is looking at my application?
What are they looking for? How do I increase my chances of getting in?
After reading this post, you’ll be armed with the knowledge to answer all of these questions and more.