I just got accepted to a PhD in deep learning at IFPEN. I will work there for the 3 next years, it’s a French research & innovation institute focused on energy problems. My doctorate school will be Paris-Saclay.
The thesis is focused on the usage of deep learning (which is a mix of computer science and applied math) on analytical chemistry. We’ll use these tools to solve real-life energy issues, such as characterization for biomass and helping plastic recycling process.
## What will you learn in this document?
- What brings me in the research industry?
- The process of recruitment I’ve been through
- What I could extract from this process
## What brings me in the research industry?
I must tell you that I was never the typical “good student”, in fact, I barely pass everything until getting into a computer-science in 2018 at 24 years old and finally being interested by my studies. In 2020, I joined a master’s degree to specialized in data-science. The purpose of this degree was to help me solve upcoming industrial problems. During my master, I did an apprenticeship on a chemical company: Arkema, they were very interested in my experience in chemistry and wanted me to work on industrial projects.
As a Software Engineer Apprentice for Arkema, I worked on distributed web and data-science applications. I spend my first 3-months of apprenticeship to build and deploy a machine learning powered software… It analyzed timeseries data and predict the arrival of a chemical reaction event.
During the rest of the first year, I had so many ideas about this project and wanted very much to improve it. My manager introduces me to people interested in this project and let me take time to work on new prototypes. This is where I met 2 researchers from our R&D team, we discussed about potential new evolution. I also took a lot of time outside my work to build personal data-science side-project. I was focused on timeseries analysis, reinforcement learning and natural language processing. I wanted to understand decision-making and text-analysis algorithms.
During the second year, I started to understand better how big-data and huge infrastructure works. I realized it was a huge part of the engineer job and I quite liked it, but I did not want to focus too much time on this. I rather wanted to focus on business problems.
My fellow coworker in R&D where very interested by the passion I put into my projects, they started to share their knowledge and imagine new possible projects. I realized during this period that, I had a lot of interest in R&D and innovation. At the same time, I liked engineering, but I preferred to work on innovation. I applied to a couples of Data-Scientist job and got rejected every time (while getting between 5 and 10 offers for Data-Engineer job per week)...
Most of the reason I received where “lack of experience”, or “we found better suited candidate”. Still, I continued my journey through data-science. A few months later, my coworkers from R&D came to me with a thesis subject. I instantly got charmed. It was scary but passionate, I applied, the institute proposed me a few interviews and accepted my application.
## The process of recruitment I’ve been through
This process of recruitment greatly differs from what I was used to. I had 3 interviews during which I saw 6 people. Each interview had very specific goals.
Here are what we discussed about:
- The first meeting was oriented around technics and science. It’s the moment where I met my thesis director and my supervisor. For 10 minutes I explained a paper they gave me to analyze, we talked about the paper and my analysis, the thesis, my interest in it and the institute. This took for about an hour and a half.
- The second was about writing, professional independence, and ability to fit on various work environments. This took half an hour.
- The third was about personal overview and insight, we talked about how I am learning, thinking. The interviewer wanted to make sure I had a true opinion about the research industry and was curious about my intellectual interests. We also talked about time management, path, failure, success, and humility. That was very interesting but unexpected, that was weird, but I had the impression it was necessary. This took an hour.
I assume these interviews where crafted to make sure the candidate is chosen not only by his technical skills but also by his ability to share and communicate effectively with other human being.
## What I could extract from this journey and hope to see in the future
For a long time, I thought PhD Doctors stood as strange Super Engineers. It’s not.
They build knowledge, not things. To build knowledge, they use tools like math, chemistry, physics, software development, psychology, etc. Compared to engineers, they spent much more time mastering these tools so that they can go beyond the obvious and spot the little details that make the difference. This helps PhD to build something that doesn’t already exist. It seems the “duty” of a researcher is to use its tools, to generate new knowledge.
I hope to spend 3 years sharpening my science skills, improving my speaking, and writing skills, as well as learning new ways of thinking, and solving passionate problems. I have a great will on the future to generate new knowledge and focus on building things that doesn’t already exist to work our upcoming problems.