I have two confirmed thesis based MS options at the same low-mid tier Canadian university.
My background is in CS, with a very weak undergraduate CGPA of less than 3.00 out of 4.00, nearly five years of data engineering experience, and a few published independent works. After the MS, I will definitely pursue a PhD.
Option 1: CS based MS
The coursework is more familiar and probably safer for earning a strong GPA. The research is in trustworthy AI and deployment risk mitigation. The supervisor is relatively new, but his research expectations are clear, straightforward, and manageable, which is an upside for me. With the more manageable coursework and research, I may also have more time for deeper or additional independent research.
However, I do not necessarily want to pursue PhD research in this specific topic. It may be something related, but probably not this exact area.
Option 2: Electronic systems based MS
The supervisor and research interest me slightly more, particularly hardware aware AI, software simulation, SNNs, and related areas. However, the coursework is much less familiar and likely harder due to it being electronics adjacent, I could do some CS related courses but overall coursework will most likely still be harder. Even though I built a decent research profile to compensate, my weak undergraduate grade still makes me really doubtful about handling the harder coursework.
I have not yet had a formal meeting with this supervisor about research expectations or a specific research direction. However, I could see myself continuing in this area during my PhD.
Basically, one option feels safer, while the other feels riskier but may have a higher ceiling.
How much should I prioritize supervisor and research interest over coursework difficulty and GPA risk? I would also be grateful for advice on how much MS research alignment matters when applying for a PhD.