Guide: AI/ML Job Hunting in Aerospace & Literature Review (1/?)
Job advice, literature review, project suggestion
There are no silver bullets, but a dang good tip I always recommend for those job hunting (to break into AI/ML in engineering, specifically) is to participate in ‘competitions’. They are few in number, and they are not always easy to find, but they are a great resource that usually are 100% free. Let me explain.
Often, prestigious research organizations (AIAA, ASME, …) have persistent topics that are always of significant importance to society, year over year. The ‘best’ turbulence model to use in a specific application, predicting high-lift scenarios accurately in aerospace, modeling best practices in automotive industry simulation, etc. So, committees are formed to host annual workshop ‘challenges’ (usually in conjunction with conference events) to get a hands-on evaluation at the latest and greatest developments relevant when solving those problems.
What’s fantastic about this for those that are job hunting:
They are usually pretty open regarding who can participate
You are often given all you need to complete the modeling: data, rules, geometry files, meshes, etc.
You are often accompanied in such workshop teams by those in industry who are making big contributions in the area. So you have a rare chance to do good work and get it recognized by those who might be hiring! Networking gold. Of course, just be professional, considerate, and not too forward.
Sometimes, these exist fully online (think about Kaggle) and therefore come with no cost for travel, registration, etc.
You can add this as deliverable experience to your resume. Resumes that simply list “I took an online class on data science” don’t have much ‘proof’ you are equipped to complete a project on such at work (hate to say it like that). However, if you have things listed that clearly demonstrate you not only have the skill, but applied it to a project successfully, then that’s a different story.
Well, the focal point of this post is that I have stumbled upon a new competition right at the intersection of machine learning and aerospace. It seems to only have recently started, so you are not even far behind.
I am not sure how deeply I will participate in this one, simply due to my commitments for the next few months. But I have a lot of experience on this topic and will proceed to at least provide best practices, tips, and recommendations on how I would do it (probably with some example codes). If you want access to this information I ask that you kindly subscribe in recognition of the weekend hours I spend to enable my readers.
Let’s start with a literature review — AI-Driven Aerodynamic Surrogate Models: A 5-Year Review