This is not a medium entropy alloy, it's a standard alloy in terms of the ratio of components, which forms medium entropy intermetallic precipitates which gives the alloy it's properties. Intermetallic MEA is an odd term I'm not really familiar with and would want to look into more, but is a little suspicious. Furthermore, while MEAs (3-4 equal primary components) and HEAs (5+ equal components) do have good mechanical properties, I'd be wary of the atomic size argument, last time I've been involved in it, that argument has increasingly been questioned, as the atomic size of the elements in question are generally pretty similar.
Feel free to reach out to the email above, I think it's one of the most interesting jobs I could have gotten coming out of a postdoc due to the mix of physics and real world, technically interesting problem solving.
I don't speak for the whole company, but in our project we are heavily Python based, Julia might be used by the central research team or something though.
Zeiss SMT is the leader in extreme engineering optics for semiconductor manufacturing. We come up every so often on HN as leaders in EUV optics manufacturing for ASML scanners. SMS is a branch of SMT concerned with standalone tooling for quality assurance and defect inspection in semiconductor fabs (tools like the AIMS EUV or PROVE systems, for example). We bring complete solutions to the hypercompetitive market that is the semiconductor industry. The scale is vast – the work you can be involved in ranges from simulations at the nanometer to how to ship tools via using Boeing 747(s).
Software (and extremely diligent/precise engineering… honestly it blows my mind regularly) underpins everything we do and spans the same vastness of scale. We (Zeiss SMT, but also SMS where I work) are hiring in pretty much every IT field as we modernize systems, upgrade legacy code, and build cutting-edge new tools and platforms to remain at the front of the field. Specifically interesting to HN (I think) is my team, which works on developing extremely fast physics simulation and data processing code utilizing classical algorithms, machine learning, and _AI_, all on premise. We also build and maintain the hardware (HPC clusters) to enable these developments.
Desirable skills
Python (scientific, HPC, ML, physics simulation), image analysis (big, big data processing, very fast), CUDA, C++, HPC architecture design/on premise rack configuration, HPC admin, DevOps infrastructure (physical as well as CI/CD), GPGPU programming, FPGA programming, QA engineering, Kubernetes
I can post some job links tomorrow, but if you have questions please either comment or reach out to me at thomas dot pekin at zeiss dot com. I’ve been working here for a year and it’s a lot of fun.
Grad students at Cal cost a professor ~100k a year, and then leave after 2-5 years with any optimizations they might have personally made. They also only work 6-12 hours a day, and having been said grad student, get mind numbingly bored after about 10-15 repetitive syntheses, spending lots of time on them, when the (only) interesting part, is the XRD pattern at the end... I would have absolutely advocated for such an arm if I was still there.
Some interesting quotes (can be found in other accounts as well):
For Mr. Cook, the focus on Asia “came down to two things,” said one former high-ranking Apple executive. Factories in Asia “can scale up and down faster” and “Asian supply chains have surpassed what’s in the U.S.” The result is that “we can’t compete at this point,” the executive said.
“The entire supply chain is in China now,” said another former high-ranking Apple executive. “You need a thousand rubber gaskets? That’s the factory next door. You need a million screws? That factory is a block away. You need that screw made a little bit different? It will take three hours.”
So, from what I've read, there is also the huge factor of being able to easily source parts, much easier than in the US, and also much easier to change a factory.