[philipkglass]

Try to make contact with grad students and postdocs working in research groups that align with your aims. Find out what the most annoying/repetitive parts of their research are and see if you can think of improvements matched to your skill set. (This could be as simple as joining a Facebook group, forum, or mailing list, and reading what people complain about.) It could be something you can improve with software alone. I'm guessing that there are unmet equipment needs too. PIs are not always incentivized to care about improving the labor efficiency of their underlings.

An uncle of mine got his PhD and then after his postdoc spent the rest of his working life helping other researchers at his university build equipment to support experiments. He had skills with machining and design that most of his colleagues lacked. Much of hands-on scientific research can be improved by some not-too-complicated piece of equipment, but that equipment isn't available off the shelf yet. Some researchers are lucky enough to be supported by people like my uncle, or already have skills like my uncle. Most biological researchers aren't also engineers. You might advance the productivity of biological research, maybe even do well financially if you design something that a company like Millipore ends up acquiring.

Tedious pipetting work used to be a major waste of time in some kinds of biological research. There are commercial robots for that now. What's the next most tedious thing that could be improved with good tooling? I don't know, but you might want to see if you can find out.

EDIT: I'm suggesting that you look for opportunities regarding experimental research instead of pure software because I'm not sure current experimental data is good/abundant enough. I was peripherally involved with an academic "proteomics" software effort more than a decade ago (is that still a trendy thing?) and my experiences led me to believe that experimental reproducibility and throughput needed to improve before it was worth focusing on software. I also hear biologists gripe about slow, poorly reproducible cell experiments in places like the comments on Derek Lowe's blog.

My personal hobby is computational chemistry but if I wanted to make a real impact on chemistry I think it would have to relate to instrumentation or tooling for bench chemists. Chemistry and especially biology are too complicated for theoretical/computational approaches to contribute much without collaborating with experimentalists.

[Koshkin]

> too complicated

More specifically, the difficulty is in developing computationally efficient models (i.e. algorithms that could be used on today's computers) - vs. just using computational methods of quantum mechanics, which in theory should be able to model anything that consists of atoms but in practice turns out to be too computationally intensive.

[philipkglass]

There are molecular mechanics/molecular dynamics methods that use only classical physics ("ball and spring" models). That's part of computational chemistry.

To elaborate on your comments, most of computational chemistry does use quantum mechanical models, and there are indeed difficult problems with computational intensity. Basic quantum chemical methods start with a big-O time complexity of O(N^4). The "gold standard" of computational chemistry, CCSD(T), is O(N^7). It is the worst-scaling method that still sees routine use.

https://en.wikipedia.org/wiki/Ab_initio_quantum_chemistry_me...

An "exact" [1] approach to electronic structure calculations, full configuration interaction, scales as O(N!) -- yes, factorial. Not surprisingly, the size of systems tractable via FCI has not grown much in 30 years even as computers have grown much faster.

There is indeed a lot of work applied developing efficient approximations to the "exact" quantum mechanical solution, and to eking out more constant-factor improvements from existing algorithms.

There's also a lot of work on taking electronic structures, available from various methods, and deriving familiar chemical properties from them. Things like NMR spectra, Raman spectra, pKa, melting point, aqueous solubility...

Measuring properties of bulk condensed-phase matter in the lab is easy but it's hard in simulation. Something "basic" like melting point is very hard to derive from ab initio calculations. On the other hand, properties that require expensive equipment to measure, like NMR spectra, are comparatively easy to calculate.

[1] Terms and conditions apply. Consult Helgaker et al. "Molecular Electronic‐Structure Theory" for details.