Here's how you can unlock career growth opportunities in Automotive Engineering.

After a good university comes the holistic experience. Being a specialist in a certain area of knowledge can result in being less employable in the event of a market downturn. Ideally, after specialization I would recommend opening your horizons and getting to know different areas. For example: if you are a powertrain specialist, you also need to worry about electronics, as the engine is increasingly dependent on precise electronics. If you already know powertrain and electronics, it's time to specialize in AI. With the likely changes in propulsion fuels, more than ever we have long-life learning in automotive engineering.

1. Attend industry events to meet potential employers and collaborators. 2. Join professional associations to stay updated on trends and connect with experts. 3. Leverage LinkedIn to showcase your skills and build a strong network. 4. Collaborate on innovative projects to demonstrate your problem-solving abilities. 5. Seek out mentorship opportunities to gain valuable insights from experienced engineers.

Nothing can be smarter than the development of connection and batteries. These are two fronts of research that, regardless of the fuel of the future (e.g. biofuels, electricity, hybrid, fossil fuel, hydrogen, etc.), will require a lot of development. The car will be increasingly connected (vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-everything and even vehicle with its systems) and we will always have electricity, so storing it will always be a necessity. Study software, study sensors, study the physics behind each fuel.

Development of V2X provides vast career opportunities as the whole infrastructure surrounding e-mobility will have to be transformed and upgraded. This is one of the key growth drivers of the industry and yet untapped source of employment opportunities, where automotive connectivity skills will be required across all verticals.

You must go far beyond the applications of technology. Seek the roots of knowledge. For example, being a user of AI software is not enough. What do you know about generative adversarial network? Are you fluent in AI math? How do you intend to talk to developers of AI alternatives if you don't even know the roots of mathematics? In context: the developer knows AI but you are the one who knows the car. What will the test plan look like? Therefore, eyes wide open: one eye on applications and the other eye on the knowledge bases.