I joined a PhD in deep learning, I will work at IFP Energies nouvelles 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 on analytical chemistry. We will 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?
That is actually a very good question since I was never the typical “good student”. I barely pass everything until getting into a computer-science in 2018 at 23 years old and finally being interested by my studies. In 2020, I joined a master’s degree to specialized in data-science. Its purpose 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 both on data-science and a few distributed web 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 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 and we discussed about potential new evolutions. I also took a lot of time outside my work to build personal data-science side-project, especially focused on timeseries analysis, and also few projects about reinforcement learning and natural language processing because 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 spend too much time on this because I prefered to focus on business problems.
My fellow coworkers 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 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 then 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 different goals.
Here are what we discussed about:
- The first was oriented around technics and science. It was the moment I met my thesis director and my supervisor. For 10 minutes I explained a paper they gave me to analyze a week ago, we talked about the paper and my analysis, the thesis subject, my interest in it and the institute. This meeting took for about an hour and a half.
- The second was about writing, professional independence, and ability to fit on various work environments, it 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, for about an our of time management, path, failure, success, and humility. It was interesting but unexpected and weird, but I had the impression it was necessary.
I assume these interviews where crafted to make sure the candidate were chosen not only by his technical skills but also by his ability to share and communicate effectively.
What I could extract from this journey and hope to see in the future
Previously, I thought PhD Doctors stood as strange Super Engineers, they are not.
They build knowledge, not things. To build knowledge, they use tools like math, chemistry, physics, software development, psychology, etc. In comparison to engineers, scientists spent much more time mastering these tools so that they can go beyond the obvious and spot little details that make the difference. This helps scientists to build thing that does not exist and it seem the “duty” of a researcher is to use its tools, to generate new knowledge.
I hope to spend 3 years sharpening my 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 knowledge and focus on building things that does not already exist to work our upcoming problems.