Just a heads up to those who haven't published in peer review journals: hyping the potential commercial applications of your research, no matter how mundane the discovery, is required.
Worse yet, the published work concerns simulations. Simulations are cool, and have great value, but they don't constitute discovery. Confirmation of a theoretical prediction by experimentalists constitutes a discovery.
Even if the effect is confirmed, the road to putting it into commercial use is long and most likely a dead end. I know first hand that "nanoscience" isn't so much science as it is an art. Tiny imperfections result in large changes to desired effects. This is due to the increased contribution of interfaces and surfaces relative to the bulk. The very same property that leads to novel physics is the one that defeats the potential for practical applications.
I long for the day when people can do good science just for the sake of good science, and not have to spin every single paper as being the start of some new revolution that never seems to come.
Gigs seem to fall into a couple categories: one group of clients has heard all the buzz and wants to get in on the action, without really knowing much about it.
The more serious types are indeed protective of their data, intellectual property, and processes. I have even seen paranoia around leveraging open source frameworks (Tensorflow, Pytorch) out of fear of their sponsor corporations coming for the client... not sure I see the logic on that one, but whatever.
Whenever I don't have much success in a venture, I try to look inward to see what I'm doing wrong. I suspect that there is a fair amount I have to learn about getting good gigs and being successful in the freelance game. Unfortunately, it's one of those "have to learn the hard way" type of things.
Great content. I've been doing freelance machine learning work for a while, and it's hit or miss. Some clients are great, others are a complete pain.
Most recent gig I had, the client cut hours and then rates... all due to the fact that the CEO didn't manage the project properly from the outset, and they were hemorrhaging cash. That fell squarely into the "not my problem" category, so I quit.
It's a tough business, and one I've not completely figured out yet.
Very cool stuff. To what extent would you recommend people study the underlying physics of QM, vs. the more domain specific content of quantum computing?
I got my PhD in physics back in 2012, after doing my dissertation in spin dependent transport phenomena in magnetic materials.
got a job at Intel as a back end process engineer, which was pretty cool, but I was then laid off in 2015.
These days I'm all about machine learning. Teaching myself by creating content, and hopefully educating others. It's been really cool seeing the overlap in some concepts (systems seeking minimum energy vs. gradient descent) between ML and physics, and I'm hoping my background will pay dividends as I get deeper into the field.
Worse yet, the published work concerns simulations. Simulations are cool, and have great value, but they don't constitute discovery. Confirmation of a theoretical prediction by experimentalists constitutes a discovery.
Even if the effect is confirmed, the road to putting it into commercial use is long and most likely a dead end. I know first hand that "nanoscience" isn't so much science as it is an art. Tiny imperfections result in large changes to desired effects. This is due to the increased contribution of interfaces and surfaces relative to the bulk. The very same property that leads to novel physics is the one that defeats the potential for practical applications.
I long for the day when people can do good science just for the sake of good science, and not have to spin every single paper as being the start of some new revolution that never seems to come.