jritchie

  Location: Edinburgh, UK
  Remote: Yes
  Willing to relocate: No
  Technologies: Machine learning, statistics, deep learning, data science, Python, PyTorch, Numpy, scikit-learn.
  Résumé/CV: https://jmsrt.ch/docs/james_ritchie_cv.pdf
  Email: hnhired@james.jmsrt.ch
  Website: https://jmsrt.ch

  Location: Edinburgh, UK
  Remote: Yes
  Willing to relocate: No
  Technologies: Machine learning, statistics, deep learning, data science, Python, PyTorch, Numpy, scikit-learn.
  Résumé/CV: https://jmsrt.ch/docs/james_ritchie_cv.pdf
  Email: hnhired@james.jmsrt.ch
  Website: https://jmsrt.ch

  Location: Edinburgh, UK
  Remote: Yes
  Willing to relocate: No
  Technologies: Machine learning, statistics, deep learning, data science, Python, PyTorch, Numpy, scikit-learn.
  Résumé/CV: https://jmsrt.ch/docs/james_ritchie_cv.pdf
  Email: hnhired@james.jmsrt.ch
  Website: https://jmsrt.ch

Probabilistic Programming & Bayesian Methods for Hackers [1] by Cameron Davidson-Pilon is exactly what you want, starting from a computational-first perspective, then introducing the maths later, although it uses PyMC rather than Stan. It's freely available as a set of Jupyter notebooks, as well as a printed edition.

[1] http://camdavidsonpilon.github.io/Probabilistic-Programming-...

Sure. In 2014. Not in 1969.

The LM computer on Apollo 14 had to be updated in flight in order to bypass a faulty abort switch.[1] The patch was applied by radioing the instructions to the crew and having them enter it manually.

[1] https://www.hq.nasa.gov/alsj/a11/a11.1201-pa.html

Approximately 7,000 residents [1], but over 400,000 employees. [2]

[1] http://www.cityoflondon.gov.uk/services/environment-and-plan...

[2] http://www.cityoflondon.gov.uk/business/economic-research-an...

In London, tech does not rule the city. No single industry does. Not even finance.

Apart from the City, which is quite literally ruled by the finance industry. [1]

[1] https://en.wikipedia.org/wiki/City_of_London#Elections

OpenCV 3 supports Python 3[1], but it's not distributed through PyPI.

[1] http://opencv.org/opencv-3-0.html

Yes, but the value of the collected data is much reduced if you can't distinguish between legitimate IMSIs and spoofed IMSIs of devices that were never actually there.

Do IMSI catchers have any way of verifying the intercepted IMSIs are legitimate? If not, would it be possible to build a device to flood them with fake/spoofed IMSIs?

Location: London, UK

Remote: OK

Willing to relocate: Western Europe, for the right opportunity

Technologies: Backend Development, Machine Learning, Computer Vision, Python, Django, Android

Résumé/CV: https://drive.google.com/file/d/0B42Td33FXXC3U1M2LXhwcnUwSTA...

Email: james.a.ritchie (Gmail)

Github: JamesRitchie

Junior engineer with an interest in data science, additional experience in backend development. Ideally looking for teams with interesting problems they want to solve with machine learning.

All of your internship adverts are out of date. (Start date 3 months past.) Are they still current?