Talking About Your Research During Interviews

Step 1 of 4

Basic Interview Preparation

What to do before the interview day

1. Research the Faculty Mentor: Look up their past publications, check their laboratory website, LinkedIn profile, or CV. Note the primary model systems they use (e.g., cell cultures, lobster perfusion, clinical data) and the core themes of their research.

2. Tailor Your Resume: Format your CV to emphasize coursework, lab courses, coding abilities, or project work that directly correlates to their research methods.

3. Bring the Right Materials: Make sure to bring:

Printed copies of your resume & cover letter.

A professional notebook and pen to jot down notes.

A prepared list of questions to ask the interviewer.

Dr. Avery asks: "Tell me about a time you disagreed with a colleague or lab partner. How did you resolve it?"

During a project in a computational lab, a teammate and I disagreed on data normalization. He wanted variance stabilization, and I preferred a log transform. I suggested we both run test cases on a small subset, check the data distributions, and present results at the group meeting. We ended up using my log approach but included his as a sensitivity analysis. It made the team collaborative.I was working on a lab report and my partner wanted to use a bar graph but I knew a scatter plot was better. I just drew the scatter plot myself and turned it in because I knew I was right and we ended up getting an A anyway.We disagreed on how to write the conclusion of our biology report. I decided to talk it out with him and we agreed to compromise by writing a paragraph that combined both of our thoughts so we both felt happy.

Dr. Avery asks: "You are given a dataset measuring stress levels during finals. How would you use Python to see if stress is correlated with sleep hours?"

First, I'd load the sheet into a Pandas DataFrame and extract the sleep_hours and stress_score columns. Next, I'd plot a scatter chart using matplotlib, labeling the axes clearly. Finally, I'd calculate the correlation coefficient using scipy.stats to check for a linear relationship.I actually don't know Python very well yet, so I'd probably write a script in R instead, or copy-paste the data into Excel and make a chart there, since Excel is much easier for me.I'd load the data in Python using pandas. Then I would make a plot to see what it looks like, and run a stats package to calculate a correlation. I don't remember the exact commands, but I'd look up the syntax on StackOverflow.