This is simply a consequence Of a weeding out mechanism for those that have no coding skills. The only ppl who get away with no coding skills are important professor with grad students to do the coding.
This isn’t to say that our skills are great, but a generic programmers (I.e. CS majors) science abilities are approximately zero (common, no thermo in an “eng” undergrad???)
So what can you do?
Since you mentioned science and not engineering, I’d ignore the AI advice. Science needs models based on mechanistic understanding of the underlying phenomena. A model that merely predicts is useful for engineers, not scientists.
“materials simulator (eg how can we get a material having a given set of properties)“
This is already done, but of limited usefulness. First the Mtls simulators are far from perfect. Then there is the problem of actually synthesizing the mtls. These simulations are more typically done to weed out bad candidates.
“No immediate financial return”
Wrong attitude. Only an attitude of “no financial return” helps science. That’s not to say you won’t make money off of it, but that can never be a goal since (true) science advances freely (again see the Gaussian jerk vs. Einstein or Landau - who contr. more?)
Instead, focus on making the programming tools scientists use better, easier to use and GPL. GPL is important because an MIT license by itself allows a scientist to use others work while blocking others (see Gaussian).
For example, making python (or Julia?) better would be one of the most important contributions you could make. The matplotlib guy was deeply mourned in science.
The two cents of a physical sciences researcher who once flirted with the Valley.
Scientists code better then you do science.
This is simply a consequence Of a weeding out mechanism for those that have no coding skills. The only ppl who get away with no coding skills are important professor with grad students to do the coding.
This isn’t to say that our skills are great, but a generic programmers (I.e. CS majors) science abilities are approximately zero (common, no thermo in an “eng” undergrad???)
So what can you do?
Since you mentioned science and not engineering, I’d ignore the AI advice. Science needs models based on mechanistic understanding of the underlying phenomena. A model that merely predicts is useful for engineers, not scientists.
“materials simulator (eg how can we get a material having a given set of properties)“
This is already done, but of limited usefulness. First the Mtls simulators are far from perfect. Then there is the problem of actually synthesizing the mtls. These simulations are more typically done to weed out bad candidates.
“No immediate financial return”
Wrong attitude. Only an attitude of “no financial return” helps science. That’s not to say you won’t make money off of it, but that can never be a goal since (true) science advances freely (again see the Gaussian jerk vs. Einstein or Landau - who contr. more?)
Instead, focus on making the programming tools scientists use better, easier to use and GPL. GPL is important because an MIT license by itself allows a scientist to use others work while blocking others (see Gaussian).
For example, making python (or Julia?) better would be one of the most important contributions you could make. The matplotlib guy was deeply mourned in science.
The two cents of a physical sciences researcher who once flirted with the Valley.