SpectralName

  Location: NY State, USA
  Remote: Yes
  Willing to relocate: no, but easy access to NYC/Boston for onsites
  Technologies: Python, Django, Postgres, JS, React, computer vision foundation models, LLMs, GIS
  Résumé/CV: https://drive.google.com/file/d/1xfpNaajacqC46E-P5PuQwn9KsG7lUmfa/view?usp=sharing
  Email: [email protected]

4 years as a research engineer, 2 years as a full stack product engineer in the Python ML and web ecosystem. I like working on open source with scientists and other talented, technical SMEs and force-multiplying their efforts through code. If you have a strong case that your org is net positive for the world, especially by helping accelerate the clean energy transition or similarly-aligned, let's talk. :)

  Location: NY State, US
  Remote: Yes
  Willing to relocate: No
  Technologies: Python (BE - Django, FastAPI; data science - numpy/pandas; ML - pytorch/ONNX/torchscript/Triton), Kafka
  Résumé/CV: https://drive.google.com/file/d/1xfpNaajacqC46E-P5PuQwn9KsG7lUmfa/view?usp=sharing
  Email: [email protected]

MLE with 5 YoE - 4 in computer vision R&D, 2 with LLMs and SaaS. The arc of my career so far has been a slow evolution from Research Engineer towards AI-assisted full stack web dev. I'm interested in working in AI4Science, climate tech, or anywhere with a compelling story about how you're making the world a better place. I'm comfortable figuring out whatever needs doing and doing it in a small org, but I'm happiest when I get to implement a paper every so often, and when I get to help technical but non-programmer SMEs solve problems they didn't realize could be solved.

  Location: NY State, US
  Remote: Yes
  Willing to relocate: No
  Technologies: Python (BE - Django, FastAPI; data science - numpy/pandas; ML - pytorch/ONNX/torchscript/Triton), Kafka
  Résumé/CV: https://drive.google.com/file/d/1xfpNaajacqC46E-P5PuQwn9KsG7lUmfa/view?usp=sharing
  Email: [email protected]

MLE with 5 YoE - 4 in computer vision R&D, 2 with LLMs and SaaS. The arc of my career so far has been a slow evolution from Research Engineer towards AI-assisted full stack web dev. I'm interested in working in AI4Science, climate tech, or anywhere with a compelling story about how you're making the world a better place. I'm comfortable figuring out whatever needs doing and doing it in a small org, but I'm happiest when I get to implement a paper every so often, and when I get to help technical but non-programmer SMEs solve problems they didn't realize could be solved.

Location: New York State, US

Remote: Yes

Willing to relocate: No

Technologies: python, numpy/pandas/pytorch ecosystem

Résumé/CV: https://drive.google.com/file/d/1CHWLmjOrw3-Eolc6XX0a2FMWR8u...

Email: matts10e6 at gmail

ML Engineer with 4 years experience. Mostly in computer vision (detection, segmentation, tracking), training and deploying models & coming up with cool uses for foundation models, also some time series analysis. Ask me about how I ended up arguing with a fishing boat captain about neural networks.

I want to build things that people actually use, keep up with the cutting edge of foundation model progress, and maybe publish along the way. Open to all application domains, not just vision. I like learning new things.

In addition to the other answers already posted, the neutrino may hit multiple water molecules along its path, or its decay products may hit other molecules themselves, so you get many flashes if you're lucky.

But another category of detector [1] adds additional signal by applying a strong, constant electric field vertically across the entire detection chamber (heavy noble gas, not ice, in this case). Then whatever charged particles are produced drift up to the top of the tank, are annihilated there, and you get a flash that gives you extra good localization in the z direction since you know how long it took them to get there.

[1] https://en.m.wikipedia.org/wiki/Time_projection_chamber

> Context: I maintain a video annotation tool that is mostly used for marine imaging.

Which one, out of curiosity? I'm just starting to look into this space and interested in knowing what's out there.

It was your first link, thank you! What a great site.

I read an amazing article once that described the representations of color at different processing levels in the human brain. For example, the 3 types of rods in the retina sense R/B/Y intensity, but at some point it is transformed into a different 3d representation with a R-G axis, a B-Y axis, and a greyscale intensity axis. There was some implication that this is information-theoretically optimal in some sense for representing images sampled from the natural world. Anyone know what I'm talking about?

The book you mention seems to cover similar ground: http://www.yorku.ca/eye/toc.htm