[dekhn]

focus on microscopy. if you have an optical engineering degree you could learn how to build a modern microscope using thorlabs components. then find some labs that need a scope person.

you shouldn't expect, with your academic pedigree and work experience, to be able to pick up enough biology to be truly useful for deep discovery. You can help out writing code, but don't expect to be able to design, run, and analyze the results of an experiment. In biology, it takes decades to be able to judge the results (very different from computer science and machine learning).

[p1esk]

There's good probability that within next 10 years some/most of that work "judging results" will be automated through machine learning.

[throwawaymath]

Why do you say that?

[dekhn]

Well, I can speak to this since I work for a company that does this. The improvement in microscope image analysis by computers in the past 5 years has been amazing. If the trajectory continues, I would say that 25% of image analysis will be automated using DNNs and other machine learning techniques.

Areas where it won't work: any time you have new image data that doesn't resemble what the networks were trained on. In fact, most people in the field recommend training on and running inference on a single microscope and if you change scopes, you have to retrain your model! Obviously data augmentation has a lot to contribute there but there a ton of challenges.

I've actually proposed building a warehouse-scale microscopy facility within a couple miles of amazon or google data center with full realtime reinforcement learning loop. If you have hundreds of near-identical scopes collecting the same data, you can train over the variation.

[p1esk]

https://news.ycombinator.com/item?id=18828293

[semi-extrinsic]

Plus you get lab snacks if you go this route. If you order both from the US and European warehouses, you even get a wider variety of snacks.

[dekhn]

I live for thorsnacks and was trying to get a thorough from my coworker but there is too much demand