My experience of the PhD application process

tl;dr: be yourself, include things you have learnt (even if not much practical experience), and get some buzz words in the bank (make sure you use them correctly)!

Introduction

During my time as a mentor for undergraduates and teaching assistant for Masters students, I have had questions about the PhD application process. Now as a PhD student with a bunch of curated responses, the following post is the advice I gave.

The following advice could be helpful, if not: that’s OK.

What to highlight in the application essay?

Many PhD applications require an essay of a few hundred words why this programme best fits you. One of the first things to highlight is your interest in the programme/the specific research area and why you are applying.

Immediately after, follow with your current/most recent education and what you most recently learnt (modules) and specific skills. For example, you may have learnt about a specific programming language (R) via a statistical module or a complex procedure through labs.

Even if it’s a skill you briefly used for an assignment, or a module you didn’t do your best in, it’s still important to highlight because you YOU CAN DO IT and the degree you’ve done reflects that.

Not only should you mention skills from modules, but the ones you acquired during the dissertation. Dissertation projects are usually independent work that required some of your own initiation.

Furthermore, specific skills for the PhD would benefit you. If the PhD is focused in Genomics, you could talk about the statistics that are frequently involved (even if you haven’t used them).

From a personal experience (my PhD original title was using AI to bridge gaps in metadata to their genomic data in Biobanks): I wrote how I had an interest in Machine Learning (ML) and my interpretation of the research question and my initial thoughts of the methods that could be involved. Then discussed the skills I have most specific to the PhD, such as ML, statistical, and database modules, the skills learnt, and the programming languages I know. I also mentioned both past and future skills: those which I would like more knowledge in and discussed previous research projects: handling biological data (sequences) and that I independently developed my own Python package to answer the main research question for my dissertation.

It does depend on the type of PhD programme. If you are a biologist who is applying for a PhD in biology but requires computational experience: you still have a great shot, and you can talk about your willingness to learn computational skills to answer the biological question. Don’t forget about the other interpersonal skills: enthusiastic, and independent, communication/presenting. Mention all those you would best describe yourself!

What was the interview procedure?

I actually remember my PhD interview very well! I walked in, introduced myself, and shook everyone’s hand. There was my supervisor, someone from HR, and staff who were a part of my funding body - however some people only a supervisor and a postdoc present.

It’s quite common to present something, usually the prompt is a recent project. My interview required that I discuss “my best piece of research”, so I talked about my Masters (MSc) dissertation.

After introductions, I led straight into a presentation. Which afterwards, the panel asked questions about. After the presentation and presentation questions, the question was directed about me. My supervisor was quiet for most of the interview and asked the technical questions.

The presentation

As mentioned, my presentation was my MSc dissertation - I enjoyed and learnt a lot and confident to present that work. I started with a little introduction about myself, my name, and that I studied my MSc in Data Science.

My presentation had the following structure:

• introduction: “looking at a group of sequences from a fairly new phylum with many unclassified” - I had a clear research aim and introduced the bacteria.
• brief literature: “researched that GC content of the sequences from the phylum seem to be consistent in groups” - I presented that I could do background research.
• question: “can I determine which group an unclassified sequence belongs to based on the GC content?” - a clear and defined research question based on literature.
• methods: “developed a Python package, grouping sequences based on current available data of GC content” - technical skills should be mentioned here: coding in Python, analysed sequences, obtain and sort/classify data.
• outcome: “sequences belonging to a specific group were correctly placed - those unclassified sequences were grouped based on GC content: this could be useful for future knowledge of the phylum” - I contributed something to science! I also mentioned some improvements: “with more time, a ML model can be applied”.

The importance of the presentation is showing communication skills, current ability, and enthusiasm. Some panels instruct different times: some only request a 7 minute presentation then 3 minutes after for questions covering the talk.

Someone was requested the following prompt: “Something about yourself that the panel would find interesting”. This is an open question with a lot of potential, though I do understand the struggle of what to present. Perhaps you could talk about a job during your industrial year, a personal project, or your coding history?

Highlighting skills in the presentation

For those who have to write an essay for the application, skills should be mentioned here - but they can also easily be mentioned in the presentation. It’s all about buzz words. If you’re presenting a recent coding project, you can throw in the programming language or how you uploaded code to GitHub.

Less technical skills are worth mentioning: presentation/communication skills, a keen learner/independent student, or enthusiastic. The panel might ask skill-related questions at the end - this is a great opportunity to highlight more! In a personal experience, my supervisor asked about how I’d handle a dataset and asking me to expand in terms of machine learning.

Interview questions

After a presentation, usually the first few questions are regarding the presentation itself. One - often difficult - question is “what would you do differently?” this means critiquing your own work. Usually research projects have an improvements/future section to consider so it’d be worth recalling those.

I found the panel wanted to know things about me. For example, my funding body wanted to know if I’d be the type of person who would involve myself in activities. They asked questions like, “how would someone describe you?” and the question everyone dreads, “what is your biggest weakness?”. I’ve found this particular question benefits from honesty but also a reflection on how you’re working on this. For example, I mentioned my biggest flaw was being shy, but that I’ve overcome a lot of obstacles since my undergraduate days by involving myself in conferences and I’m still working on this. The panel seemed to appreciate the honesty and self-reflection.

The questions then directed to a technical area and about the PhD: “do you enjoy research?” and “what do you think about the PhD project?” of course it’d be ideal that you enjoy research to pursue a PhD. The technical questions may be specific methods you mentioned in the presentation or skills needed for the PhD. An important thing to remember here: it’s OK to say you don’t know a particular skill - even if you’ve not heard of it - as you can you show a willingness to learn.

One question I remember clearly from my supervisor, “what would you do if I gave you a dataset of patients with heart disease?”. Knowing this project was ML-based, I decided to try and go for that angle, despite the vagueness. I mentioned data cleaning is the first step and then said either a supervised or unsupervised method based on the research question. My supervisor seemed to like my response but wanted to know more and asked what for some specifics. I vaguely remember saying something similar: “Using Python I would do unsupervised clustering to look at patterns in the data” (showing skills).

Trick questions

This can be quite subjective - my opinion on a tricky question can differ from someone else’s. Although I was prepared for my interview (research topic and presentation) and did some background research into the supervisor - I didn’t read any of their papers!

A question I received, “out of all your supervisors’ papers, which one did you like the most?”. This one personally I thought was a “trick” question (someone I spoke to afterward disagreed and said this was a question to be expected). But personally, this question caught me off guard! I knew my supervisor’s research areas but didn’t read a paper and so I was completely honest and said something like, “I am really sorry but I haven’t read any of their papers. But I have heard of supervisor and people have spoken fondly of them”. I think they appreciated the honesty, and my supervisor seemed happy.

Final tips/comments

• The PhD is a time to learn about various methods and techniques, so try not to worry if you can’t answer a question - honest responses are appreciated.
• Be yourself: they want to see if you’re a good fit and if you are who wrote the application. The panel want to know who you’ll be as a researcher.
• Buzz words should include your skills and other relevant topics - but don’t throw them needlessly and without knowledge.
• Finally, do a little background research into the research area and the supervisor - but don’t worry about needing to know the inner details and the algorithms.

These are all my opinions and experiences - everyone’s is different - and each panel will ask different things. The PhD is a learning and development journey, and the supervisor should be passing on their knowledge.