•1 min read•from Data Science
Radar engineer upskill
Our take
As a radar signal processing engineer with a master's in robotics and AI, you’re well-positioned to transition into applied machine learning for robotics. With solid math skills and access to real data, you have a unique advantage. Your main inquiry revolves around the value of re-implementing ML algorithms versus exploring side projects or pivoting internally. Consider the impact of each option on your career trajectory. Engaging with professors for research could also provide valuable insights.
Hello all,
I’m a radar signal processing engineer (point clouds, spectrum analysis, lots of legacy debugging) and want to move into applied ML for robotics. I have a masters in robotics and AI.
I’ve got solid math + sensor data experience, and access to real data plus an internal repo with ML projects.
My main question: is it worth spending time re-implementing those ML algorithms myself plus doing side projects, or is not worth it.
I can dedicate 2 hours a day for the projects. I am very serious about leaving, but i lack direction.
Would you:
- Stay and build projects on the side?
- Try to pivot internally?
- Or consider something like try to do research with a professor?
If you’ve made a similar move, what actually helped you break in?
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