91̽»¨

Image

"To succeed in a position like this, you need to know how to learn by yourself. Because if you want to do leading research, you always need to learn new things" says Razan Ghzouli.

Razan’s path to life as a PhD student started with a bachelor’s degree in computer and automation engineering in Damascus. She got a scholarship to go to the University 91̽»¨ to study her master’s in applied data science. She graduated in November 2019 and started her PhD position shortly afterwards.

"Being a PhD student is actually something I never thought I would be, but the opportunity appeared, and I thought it sounded interesting. I got a chance to interact with the professor who posted the position. I really liked the topic itself, the teachers and the supervisor. Chalmers, where I’m employed, is a top university – so I thought, why not apply?"

Demanding but rewarding

Razan is a part of a research group in the Software Engineering division that focuses on autonomous systems – more specifically robots, AI and machine learning.

"I work with robotics, and specifically the safety of mission for robotics. The hope is to establish these methods and tools to ensure that the mission of the robot is doing what it is supposed to be doing, that there is no problematic scenario. So I am extracting the behavior model of a robot, looking for these problematic scenarios."

According to Razan, being a PhD student is both demanding and rewarding.

"In a PhD position you learn different skills. You learn to multitask, you learn how to manage yourself – of course your supervisor is always there, but if you don’t learn to manage your time it will be hard to move on with the PhD. That can be a really challenging experience."

She continues:

"I think the most important skills to possess in order to succeed as a PhD student are self-learning, time management and persistence. It’s sometimes even more important than knowing the topic."

Razan, what brought you into the field of applied data science?

"It started during my bachelor, where I learned a little bit about a lot of things. Some parts where related to AI and machine learning. We didn’t talk about data science specifically, more about how to apply machine learning on a topic. After my bachelor, I worked for 2 years. My first job was within communication where I worked with key performance indicators for networks. I did a lot of things manually, and I realized I wanted to learn how to do these things in a smarter way. My second job was humanitarian work, and that was where I realized the need of extracting information from data and analyze it, to try to find patterns that can help people. Then I felt more eager to get a scholarship and do my master’s within data science."