Herrin
Joined 9 years ago
Joined 9 years ago
> Possibly even worse is the fact that so much academic writing is kept behind vastly more costly paywalls.
The time scales are much longer, unfortunately. Instead of taking one or two years to get a company off the ground, it can be more like five for an experiment[1]. I'd attribute this to slower iteration. You end up throwing away or rebuilding a lot of physical things, many of them bespoke, on the way to a working apparatus.
Also, the compensation is much worse for experimental physics. For he first decade of your career, you work as a grad student, then a post-doc, for slightly above poverty wages. This does have the advantage of meaning you work with very motivated people, but you're giving up a lot for that.
[1] The XENON experiments from the article (10, 100, and now 1 Tonne) have been developed over about 15 years, for example
I'm really interested in Futhark, though I haven't found a project where it would be make sense to use it. But I feel like it has the same potential to make GPU programming not feel overwhelming the same way Elm did with frontend work for me.
The first has to do more with the nature of the mass than the mass itself. In the standard model, electrons, muons, and taus get their mass from the Higgs field. There's a way for neutrinos to get their mass in other ways, but it requires them to be their own antiparticles. And this gives a satisfactory answer as to why their masses are so tiny, and suggests some new particles (although at electroweak unification scale, so not anything we're going to achieve with a collider any time soon). There are a number of double-beta decay experiments trying to measure the Majorana mass of neutrinos.
The other would be if the neutrino hierarchy is "inverted". The tau is heavier than the muon is heavier than the electron. Right now, with neutrinos, we can only measure the difference in masses. And so it's not clear if the neutrino with the most election portion[1] is the lightest or if the neutrino with the most tau portion is. The latter would be "inverted" from what we expect, and trying to figure out why might be interesting, though I don't know that it immediately implies new physics.
There're also other things related to masses that are interesting. Neutrino oscillations are determined by the differences in mass. Looking at these, we might be able to discover more generations of neutrino (beyond electron, mu, and tau), which would be new physics.
[1] the mass eigenstates of the neutrino (that is, the things with well-defined masses) are not weak force eigenstates, so they contain mixtures of the electron, mu, and tau neutrinos
When things swing the other way, and not as much money is floating around waiting to be invested, investors will be able to hold the investees to higher standards.
Trying to find the documentation for how to draw an arrow on a plot was always fun, because searching for "TArrow ROOT" would inevitably get results for "taro root".
The XRootD[2] project is pretty interesting, though, and I feel like the software industry is going to have to start dealing with similar data problems before too long.
[1] https://root.cern.ch/doc/master/classTH2D.html [2] http://www.xrootd.org/
> I completed the survey which was asking me workplace situational questions testing my unconscious biases and then quickly rejected the dispute.
This sounds like the "consulting company" was someone running a scientific experiment to see who is willing to create racist/ageist/otherwise unethical models.
This is a good reminder for me to make backups of the ebooks I do have.
Pathos was founded to revolutionize precision medicine in cancer by harnessing the power of machine learning to transform drug development. Pathos aims to accelerate traditional oncology approaches based upon a comprehensive and integrated data-first strategy to discover and develop drugs for precise biological pathways. Pathos is currently pursuing co-development and in-licensing opportunities to build a portfolio of clinical stage oncology assets, applying its technology to find the right patients and indications, resulting in a highly improved probability of success and faster time to market.
I'm hiring for software engineers, with a focus on cloud infrastructure. We'll be developing infrastructure for data analysis and storage, processing and analysis pipelines, and internal applications. Engineering writes code mostly in Python, while our scientists prefer R. We use Google Cloud Platform for our infrastructure. Currently, the team is just me, but we are looking to hire at least 1 more full time engineer.
An ideal candidate will have at least 2 years of experience developing software in a professional environment, with at least 1 year of that working with Google Cloud Platform.
Nice-to-haves are experience with scientific computing, experience with bioinformatics tools and/or biological and medical data, and experience with Python, R, PostgresSQL, and containers. Experience securing data and applications would also be nice.
There's a small amount more about the company at: https://pathos.com/ Please send a resume to [email protected]