Does cold fusion not have sub-problems that could be tackled at a master’s level? I don’t think OP was necessarily saying that they were proposing their master’s would be about solving cold fusion completely.

It could be theoretical. The high baseline effort and cost to run even a simple experiment would probably preclude empirical work. The theoretical area is probably saturated by now, will be difficult to carve out a niche.

I feel like you could say that about any discipline that any Master's student would embark on just because the state of the art is extremely advanced, no? For example, if you're a chemical engineer focusing on batteries, you could say simple experiments are out of reach too because "simple" has advanced to a point that's extremely complex & expensive. I haven't done a Master's so I don't actually know.

Oh, no I don't think so. High-energy physics has a high cost barrier to entry. Even a simple experiment requires a complex apparatus ex. an accelerator, or a very high pressure vacuum chamber, laser cooling, Bose-Einstein condensates, etc. It's much cheaper to run experiments in chemistry, batteries on the whole are not terribly complicated. We're talking about going from hundreds of thousands of dollars to merely thousands of dollars.

You could go a step further and talk about computer algorithms or mathematics in general. The only cost there is a pen and a piece of paper. The only cost there is your own time. Some experiments and analyses cost more than others, it's just the nature of things.

My point is that when you get sufficiently advanced, you some times require access to resources that aren't just a pen and paper. So sure, some fields of math may still be ok to do with a pen and pencil. It wouldn't surprise me if some require large super-computer style access. Or distributed algorithms that are written by master's & PhDs at massive cloud/internet providers that rely on those large networks to run an experiment. So even in the CS domain I expect there to be Master's thesis that isn't cheap to reproduce.

Similarly, if you're trying to get a novel battery chemistry to outperform a Tesla car battery or generate a completely novel solar cell, you're not going to be able to accomplish that as an IC researcher. As you point out, there could easily be interim projects along the way to identify various interesting properties that might be useful to your long term goal which aligns with my original statement. When that's out of scope you partner with institutions with access to those resources whether those are big corporations, research institutes, or particle accelerators.

Batteries are one I happen to know something about, a startup I worked for hosted $major-university's monthly battery lecture series, we were doing a SaaS business targeted at the vertical. I met a dozen or so grad students doing exactly this.

So, nope, bad example. Taking an existing process and giving it a few tweaks will push it around in parameter space, improving something we care about (number of cycles before 80%, let's say), often at the expense of something else, like you can't apply as many coulombs. Congratulations, you've got a thesis. And there are dozens of basic battery processes.

To clarify it wasn't for my thesis but for a nuclear engineering course where we could research whatever. I know not all profs are the same, but generally I do think there is a bit of a herd mentality in academia. My point is that there have been roadblocks for LENR researchers in academia for quite some time, and I hope we can finally remove those.

Most definitely concur with the herd mentality. It comes from the funding model, which is admittedly rather broken. Unique or off-the-wall ideas often don't receive funding, and once you've been funded for a mainstream idea, it's hard to advocate much for the unique idea without somehow discrediting your proposal or the time you've put in to date. It's a vicious Catch-22. Most researchers start out with unique ideas, then it gets drummed out of them. My area is computer vision, and I see machine learning eating everything, so I can empathize. There seems to be no effort any more in translating a physical phenomena into a quantitive, deterministic model and that's a shame. Neural networks are not an accurate representation of what our eyes and brains are doing. But, it's produced more promising results for the time being, and so we've entered a cycle of funding that promotes machine learning over other approaches. There is recognition of this fact usually, and small pools of money continue to exist for off-the-wall ideas, so the rest of us can make do until the phase passes. Science moves in fits and starts.

Research in high energy physics (cold fusion is a sub-category), is difficult to cobble together on small amounts however. That's why you see profs and labs banding together to raise sufficient resources just for a couple of experiments. And unfortunately for LENR, a great deal of money was spent in the 50s to 70s with no appreciable outcomes, therefore the funding bodies have become jaded and cynical about continuing to fund further research. The area will likely see a resurgence once those board members retire and bright-eyed folks revisit the field.